| Project Title | Citizenship Required | Reference Code | Posted Date | Posted Datetime | Hosting Site | Internship Location | Description |
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| Yes | ORNL-Roschli1 | 12/21/2022 | 1671598800000 | Oak Ridge National Laboratory | Oak Ridge, TN or virtual |
U.S. Citizenship is a requirement for this internship Project Description:Slicing is the process of taking a CAD (computer aided design) object and generating toolpaths for 3D printing. The process uses many fundamental geometry processes to manipulate objects and develop text-based g-code output. ORNL Slicer 2 is a state-of-the-art slicing program developed at ORNL that supports more than a dozen unique additive manufacturing processes. This software package is written entirely in C++ using the Qt framework. Development of new algorithms, GUI (graphical user interface) elements, and system integration is ongoing.This project will give a student the opportunity to develop software for a variety of 3D printers as well as interact with a few. Depending on the interests of the student, learning objectives can include any of the following C++ algorithm development, development of network communication protocols for software-software data exchange, g-code formatting an interoperability, and printing on an industrial-scale 3D printer. Hosting Site:Oak Ridge National Laboratory Internship location: Oak Ridge, TN or virtual Mentor:
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| Yes | ORNL-Walters1 | 12/21/2022 | 1671598800000 | Oak Ridge National Laboratory | Oak Ridge, TN or virtual |
U.S. Citizenship is a requirement for this internship Project Description:Deposition of titanium and titanium alloys presents an opportunity for metal additive manufacturing to produce new and complex parts for aerospace, naval, and other industries. However, GMAW based deposition of titanium poses unique challenges for maintaining weld quality due to sensitivity to oxygen and reactive gases. The student will assist in conducting experimental process development for titanium deposition, including aiding with the design of a custom shielding gas device to increase the shielded (inert) volume around the weld. The student will gain experience in 3D modeling (SolidWorks), knowledge of welding and additive manufacturing, familiarity with fluid flow and general material science.Hosting Site:Oak Ridge National Laboratory Internship location: Oak Ridge, TN or virtual Mentor:
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| Yes | ORNL-Elliott1 | 12/21/2022 | 1671598800000 | Oak Ridge National Laboratory | Oak Ridge, TN or virtual |
U.S. Citizenship is a requirement for this internship Project Description:Binder jet additive manufacturing (AM) holds significant promise in providing low-cost metal components that leverage the design freedom and other benefits of AM. One challenge with binder jetting is the fragility of the parts after printing and the subsequent need for manual depowdering by a human operator. This project seeks to explore methods of automating the depowdering of binder jet parts using robotics, novel gripping components, and sensors.Hosting Site:Oak Ridge National Laboratory Internship location: Oak Ridge, TN or virtual Mentor:
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| Yes | ORNL-Post1 | 12/21/2022 | 1671598800000 | Oak Ridge National Laboratory | Oak Ridge, TN or virtual |
U.S. Citizenship is a requirement for this internship Project Description:Using a large serial manipulator the student will participate in fundamental research aimed at learning how to create near net shape steel parts directly from iron ore using a new 3D printing process being developed at ORNL. The goal of this project is to develop an electricity-based manufacturing pathway to print cast iron replacement material directly from low-cost feedstock. The project will use a welding technology based on hybrid electro slag cladding (H-ESC) combined with low-cost iron feed stock that contains direct reduced iron and iron oxide to deposit stacked beads of material with mechanical properties useful to the clean energy industry, demonstrating a clear path to large-scale casting replacement parts. The chosen participant will interface with seasoned engineers to design and carryout experiments, develop new manufacturing hardware and processes, and characterize the performance of produced components.Hosting Site:Oak Ridge National Laboratory Internship location: Oak Ridge, TN or virtual Mentor:
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| Yes | ORNL-Post2 | 12/21/2022 | 1671598800000 | Oak Ridge National Laboratory | Oak Ridge, TN or virtual |
U.S. Citizenship is a requirement for this internship Project Description:Most 3d printers can only fabricate objects smaller than the printers themselves. To solve this problem and radically reduce the cost associated with large scale composite AM technologies, ORNL has been developing the Configurable Holonomic Additive Manufacturing Platform (CHAMP) consisting of a 100Kg payload serial manipulator mounted on a high capacity omnidirectional vehicle with a 75kg/hr polymer extruder for an end effector. The selected student will learn through experience how a seasoned interdisciplinary team of engineers works together to solve complex open ended engineering problems, culminating in a public demonstration of the new system at the end of the summer (printing a 50+ft long object!).Hosting Site:Oak Ridge National Laboratory Internship location: Oak Ridge, TN or virtual Mentor:
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| Yes | ORNL-Masuo1 | 12/21/2022 | 1671598800000 | Oak Ridge National Laboratory | Oak Ridge, TN or virtual |
U.S. Citizenship is a requirement for this internship Project Description:The selected participant will work on controls and system development for MedUSA, a multi-robot large scale metal additive manufacturing system. The participant will research and develop on optimizing dynamic toolpath allocation algorithms that improve the productivity and overall performance of the system. They will also do some development in hardware to improve the quality of a 3D printed metal part. Programming and CAD will be used to implement these system improvements. Additionally, they will learn to operate the MedUSA system and control industrial wire-arc robots as well as run robot simulations. The participant will also participate and engage in meetings with other ORNL staff and industrial partners.Hosting Site:Oak Ridge National Laboratory Internship location: Oak Ridge, TN or virtual Mentor:
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| Yes | NREL-Schaffer1 | 12/21/2022 | 1671598800000 | National Renewable Energy Laboratory | Golden, CO |
U.S. Citizenship is a requirement for this internship Project Description:One of our current bottlenecks in fuel cell and electrolysis research at the National Renewable Energy Laboratory is the fabrication of electrodes for single cell testing (electrodes of 50 square centimeters or less). We currently use a proprietary coater that sprays inks onto substrates using a 2-dimensonal criss-cross pattern, but due to cost and long lead times we have not obtained additional coaters. We would like to convert a commercial-off-the-shelf 3D Printer into a spray coater.This project would start out with requirements definition to meet both functional and safety specifications, hands-on work to adapt the spray nozzle to the 3D printer in addition to safety features (i.e. interlocks), functional checkout of the installed spray coater and safety features, benchmarking of the 3D printer-sprayer produced electrodes versus electrodes produced by our standard spray coater (validation), and authoring a simple user manual for 3D printer-sprayer. The internship participant would gain experience in electrode fabrication and electrochemical performance in addition to utilizing their robotics and engineering background (i.e. mechanical design, component integration, programmable logic controller setup). Hosting Site:National Renewable Energy Laboratory Internship location: Golden, CO Mentors:
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| Yes | LLNL-Porcincula1 | 12/21/2022 | 1671598800000 | Lawrence Livermore National Laboratory | Livermore, CA |
U.S. Citizenship is a requirement for this internship Project Description:LLNL is conducting exciting research in advanced manufacturing of scintillator materials to enable new sensory applications. This capability helps perceive the world, interpret the information perceived from the world, automate analysis of data, and communicate and coordinate with humans. Measurements with high noise levels are difficult to interpret and requires manual (human) evaluation. High signal to noise levels must be reduced to effectively enable correct perception of the world. Automation of data analysis also requires reducing signal to noise levels. The end goal is enabling new sensory applications by utilization of novel advanced manufactured scintillators.3D printing and other advance manufacturing approaches may help researchers create novel new scintillators with advanced capabilities and better ability to perceive the world, to distinguish true signals from noise. Students may research advanced materials processing including polymer processing, feedstock development, and materials characterization. The student will explore different advanced manufacturing routes with relevant commercial and proprietary feedstocks for novel scintillating materials. The student will potentially develop both organic and inorganic feedstocks for additive manufacturing techniques such as binder jet additive manufacturing and direct ink writing. The student may also utilize thin film manufacturing techniques (e.g., tape casting, spin coating, electrospinning) to manufacture novel scintillator geometries with enhanced luminescent properties. The student will utilize materials characterization techniques to evaluate the resultant materials. The student will participate in testing of scintillators. The student will characterize the developed scintillators with known neutron generating sources to determine scintillator properties such as the time decay and luminescent intensity. The student may be trained to use certified neutron generating source. The student will be part of an interdisciplinary team exploring potential advanced manufacturing/fabrication routes for novel scintillators. They learn about additive manufacturing principles relating feedstock properties (e.g., rheology and viscoelastic properties) with printability. The student will gain familiarity with engineering strategies to alter feedstock rheology for advanced manufacturing. The student will learn about, and operate, multiple techniques for materials manufacturing (e.g, extrusion and powder-based additive manufacturing, thin film manufacturing, electrospinning). The student will learn various characterization equipment (e.g., photomultiplier, thermogravimetric analyzer, infrared spectrometer, scanning electron microscope, etc.). The student can attend seminars for ES&H, the Seaborg Institute (Radiochemistry), Materials and Chemistry Institute (MaCI), Materials Engineering Division (MED). Students can attend tours and other student events at LLNL. Hosting Site:Lawrence Livermore National Laboratory Internship location: Livermore, CA Mentors:
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| Yes | LLNL-Lee1 | 12/21/2022 | 1671598800000 | Lawrence Livermore National Laboratory | Livermore, CA |
U.S. Citizenship is a requirement for this internship Project Description:LLNL is conducting exciting research on utilization of RFID technologies for automated collecting and updating of data. RFIDs are quickly becoming a technology utilized in a variety of activities. This technology offers a key capability for enabling automation of manual operations for collecting data (conducting inventories) and updating data. This project provides the student with the opportunity to practice and learn about utilization of RFID readers and computer systems they interface with. This capability helps perceive the world, interpret the information perceived from the world, and communicate and coordinate with humans. The student will interact with scientists, facility managers, and multidisciplinary health and safety professionals. It is a great way to be introduced to the diverse research projects and activities at LLNL. The Chemical Inventory and Safety Data Sheet Group provides an important safety role by collecting field data of chemicals located at various LLNL facilities and enters the data into central system accessible by staff. The team tags and inventories chemical containers site-wide using inventory laptops and Radio-Frequency Identification (RFID) readers. Understanding of the utilization of RFIDs and evaluating practical implementation details is an important step to automating collection and updating of data. This application is a level of complexity beyond the collection and updating of data by automated systems in warehouses.The student will be part of an interdisciplinary team the Technical Services Department (TSD) in the Environmental Safety and Health (ES&H) Directorate at LLNL. They will learn how field data of chemicals is collected and entered into a central system accessible by staff. They will have the opportunity to broaden their practical implementation details of data collection, updating data, and chemical storage practices. They will learn how to tag and inventory chemical containers using inventory laptops and RFID readers. Understanding implementation details and potential scenarios is important. In the future, robots will be able to act on the world, collecting the information automatically, interfacing with humans, and act when necessary (e.g., to remove excessive quantities of chemicals or colocated incompatible chemicals). Students will have the opportunity for training on other ES&H field support and related activities. This individual will interact with LLNL scientists, facility managers, and multidisciplinary health and safety professionals. They may attend ES&H, Data Science Summer Institute (DSSI), Materials and Chemistry Institute (MaCI), Seaborg Institute radiochemistry, and other seminars. They may participate in student events and tours. Hosting Site:Lawrence Livermore National Laboratory Internship location: Livermore, CA Mentors:
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| Yes | ORNL-Haley1 | 12/21/2022 | 1671598800000 | Oak Ridge National Laboratory | Oak Ridge, TN |
U.S. Citizenship is a requirement for this internship Project Description:In Additive Manufacturing (AM), toolpaths are generated by CAM tools in advance, and machine controls are used to guide the printer along this preplanned instruction set. However, with advancements in in-situ sensing, such as visible and thermal imaging, 3D scanning, and other signal monitoring, it has become possible to identify defects compromising quality and re-plan toolpaths to take corrective action. This requires innovation in software integration between planning tools, such as the ORNL slicer, the machine controller, and in-situ sensing, which is accomplished through a common data platform such as the Robot Operating System (ROS). In this internship, the participant will have the opportunity to learn these tools, develop live replanning controls to optimize printed component performance, and test their work on state-of-the-art large-scale polymer and metal additive manufacturing systems. During the internship, the student will can pursue their interests in learning about advanced manufacturing, and the python, C++, and ROS tools that enable these digital manufacturing technologies.Hosting Site:Oak Ridge National Laboratory Internship location: Oak Ridge, TN Mentors:
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| Yes | NREL-Berry1 | 01/18/2023 | 1674018000000 | National Renewable Energy Laboratory | Golden, CO |
U.S. Citizenship is a requirement for this internship Project Description:Intern will develop a process module on a robotically controlled, closed-loop perovskite solar cell fabrication system. The system is used to autonomously explore perovskite film deposition conditions through high throughput device fabrication and automated characterization. This work leverages the high precision of robotically controlled systems with machine learning guided experiment selection in order to streamline research and development timelines on high-priority devices.The intern will build / develop an isolated component of the autonomous system. Depending on interest and experience level, the possible components include but are not limited to: - Substrate cleaning and storage system - Human to Robot sample transfer system - Machine vision integration for sample tracking - Robotically controlled in-situ characterization tools - Data analysis algorithms (raw data processing, data management, machine learning, etc.) - Experiment selection algorithms (cheminformatics, Bayesian methods, etc.) The intern will also work with a cartesian robot. This internship will provide the opportunity to progress programming abilities in robotics, data science, and optimization with uncertainty and build an interdisciplinary skillset for autonomous experimental system development. This project can support multiple interns. Required - Understanding of basic programming principals - Interest in learning a new programming language - Interest in closed-loop experimentation and data driven research • No prior experience in material science is required Preferred - Experience with LabVIEW, Python, and/or Matlab - Experience in robotics and/or data science Hosting Site:National Renewable Energy Laboratory Internship location: Golden, CO Mentors:
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| Yes | ORNL-Vaughan1 | 12/21/2022 | 1671598800000 | Oak Ridge National Laboratory | Oak Ridge, TN |
U.S. Citizenship is a requirement for this internship Project Description:Current manufacturing methods require machines to be larger than the parts that they are making. However, as parts increase in size, the costs of the manufacturing equipment increases exponentially. One method to combat this problem is to use swarms of smaller robots to collaboratively manufacture large parts. This approach opens many research questions, among which are how to divide the part production between the multiple agents, how to localize each agent to the precision necessary, and how to integrate the part division and localization into both global and local control algorithms. This project will investigate one or more of those topic areas, using both simulation and experiments. More specifically, mobile, multi-agent manufacturing platforms at Oak Ridge National Laboratory's Manufacturing Demonstration Facility (MDF) will be used at the application examples. Simulations that have been developed will be used to aid in the development of algorithms for the multi-agent manufacturing system and physical systems under development will be used for experimental verification.Hosting Site:Oak Ridge National Laboratory Internship location: Oak Ridge, TN Mentor:
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| Yes | ORNL-White1 | 12/21/2022 | 1671598800000 | Oak Ridge National Laboratory | Oak Ridge, TN or virtual |
U.S. Citizenship is a requirement for this internship Project Description:Industry 4.0 is revolutionizing manufacturing through the integration of automation and real-time data sharing in cyber-physical systems. At the forefront of this revolution is large-format additive manufacturing (AM). Integral to AM is slicing: the process of converting a 3D digital object into specific printing instructions. At the Manufacturing Demonstration Facility (MDF), Slicer 2 is a novel slicing engine capable of generating millions of printing instructions for large objects in a matter of seconds. Under the supervision of the mentor, the student will contribute to the development and analysis of algorithms related to the slicing process. They will learn to solve problems in AM using methods from a variety of areas including path planning, computational geometry, optimization, parallel programming, scientific visualization, and more. Their solutions will be experimentally verified and integrated into Slicer 2.Hosting Site:Oak Ridge National Laboratory Internship location: Oak Ridge, TN or virtual Mentors:
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| Yes | ORNL-Borish1 | 12/21/2022 | 1671598800000 | Oak Ridge National Laboratory | Oak Ridge, TN or virtual |
U.S. Citizenship is a requirement for this internship Project Description:Industrial additive manufacturing is one technology in the Industry 4.0 revolution. A great deal of progress has been made with the technology, especially hardware. However, software has lagged behind. One important software component for additive manufacturing is slicing: the process of turning a 3D object into the commands a machine will use for construction. At the Manufacturing Demonstration Facility (MDF), development of next-gen slicing capabilities is being developed. The student, under the direction of the mentor, will participate in the development and analysis of algorithms related to slicing for additive manufacturing systems. They will learn to solve computational problems from areas as diverse as path planning, visualization, GPU, optimization, computational geometry, and more. The solutions will then be experimentally verified and incorporated into the codebase.Hosting Site:Oak Ridge National Laboratory Internship location: Oak Ridge, TN or virtual Mentor:
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| Yes | LLNL-Mitchell1 | 12/21/2022 | 1671598800000 | Lawrence Livermore National Laboratory | Berkeley, CA |
U.S. Citizenship is a requirement for this internship Project Description:LLNL is conducting research at the intersection of biology and materials science, specifically bio-functionalized electrospun nanofibers to achieve superior extraction, selectivity, and detection. Our goal is to develop robust and efficient methods for development and automated production of biofunctionalization electrospun nanofibers. Automation is important to scaling up production levels to enable testing of these emerging technologies in several applications. Biofunctionalization could allow selective detection, indication of detection, and/or extraction of specific analytes. Under the mentorship of an interdisciplinary group of scientists and engineers, students will research, develop, and characterize materials designed to stabilize biological media for diverse applications such as swipes (for detecting contamination on solid surfaces) and extractors (e.g., for extracting select materials from contaminated water). Contamination control is a common safety practice necessary for the detection and cleanup of spilled hazardous materials. Technicians manually swipe university laboratories or factory floors or waste facilities, manually package the swipe, manually transport the swipe, manually digest the swipe, and manually apply characterization techniques to determine if contamination is present. The result of the student’s research can help enable eliminating some manual steps and automating other steps. This capability helps perceive the world, improve the sensitivity of that perception, improve the accuracy of what is perceived (is what is detected A or B that could be misperceived as A?), interpret the information perceived from the world, automate analysis of data, communicate and coordinate with humans, and act on the world. Students will learn and participate in activities related to processing electrospun nanofibers, biofunctionalization, and affiliated technologies facilitating automation. Students may research development of feedstock formulations, electrospinning optimization, structural characterization, biofunctionalization, and other advances enabling automation. Students may then test the performance of their product. The student can attend seminars for ES&H, the Seaborg Institute (Radiochemistry), Materials and Chemistry Institute (MaCI), Materials Engineering Division (MED). Students can attend tours and other student events at LLNL. Hosting Site:Lawrence Livermore National Laboratory Internship location: Berkeley, CA Mentor:
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| Yes | LLNL-Mitchell2 | 12/21/2022 | 1671598800000 | Lawrence Livermore National Laboratory | Berkeley, CA |
U.S. Citizenship is a requirement for this internship Project Description:LLNL is conducting exciting research in 3D printing/additive manufacturing as well as research on porous membranes, sealants, ceramics, gas separations, and/or filtration. One research project poses interesting opportunities to research two different approaches to 3D printing. Students may research advanced materials processing including inorganic materials processing, thermal processing and thermodynamics of materials, and materials characterization. Students will combine conventional ceramic processing techniques with additive manufacturing with the goal of creating filters with complex internal geometries that can meet and exceed requirements. This challenge requires a quantitative investigation of the capabilities of 3D printing of polymers for use as ceramic templates. The manufacturing limits of thermoplastic 3D printing, including accuracy and size, must be understood in relation to the design space of ceramic filters. This information will allow our group to develop novel filter designs that advance the state of the art. This research is directly applicable to improving safety. HEPA filtration is a critical component of safety systems in hospitals, manufacturing facilities, and nuclear facilities. The added demands of fire resistance, low pressure drop, and reusability have driven research efforts for more effective ceramic filtration technologies. Students may also research additional emerging technological areas with potentially different technical goals and include processing from powders of ceramics and metals. Additional examples of additive manufacturing opportunities include:
Education: Engineers or material scientists Hosting Site:Lawrence Livermore National Laboratory Internship location: Berkeley, CA Mentor:
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| Yes | LLNL-Mitchell3 | 12/21/2022 | 1671598800000 | Lawrence Livermore National Laboratory | Berkeley, CA |
U.S. Citizenship is a requirement for this internship Project Description:LLNL is conducting exciting research in organic and inorganic electrospun nanofibers. Our goal is to develop robust and efficient methods for development, production, and integration of nanofibers for a broad range of applications, including sensors. The student will learn and participate in activities related to the processing of ceramic nanofibers as well as polymeric nanofibers. Students may research development of feedstock formulations, electrospinning optimization, structural characterization, forming, thermal processing, process scaling, device integration/prototype development, and performance testing. Students will combine conventional electrospun nanofiber processing techniques with innovative new approaches that can meet and exceed requirements. This challenge could help address America’s need for appropriate materials for N95 masks to prevent the spread of COVID-19. The manufacturing limits of electrospun nanofibers must be understood in relation to the design space of the application. This information will allow our group to develop novel nanofiber materials that advance the state of the art. At present, nanofiber media prepared by LLNL are manipulated manually to create useable structures one-at-a-time in a very time and labor-intensive process. Thousands of these structures are required for scaling the process and testing prototypes that use these structures. Automating these rather delicate cutting, peeling, handling, heat sealing, and general mechanical manipulation processes can improve productivity to reduce time and labor requirements. Fundamentally different filter designs compared to conventional filters also require new metrics and in-line sensors for health monitoring of the filter. Hosting Site:Lawrence Livermore National Laboratory Internship location: Berkeley, CA Mentor:
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| Yes | LLNL-Ross1 | 12/21/2022 | 1671598800000 | Lawrence Livermore National Laboratory | Livermore, CA |
U.S. Citizenship is a requirement for this internship Project Description:LLNL is conducting exciting research in 3D printing/additive manufacturing as well as research on porous membranes, sealants, ceramics, gas separations, filtration, and other applications. This research opportunity is to develop a graded, multimaterial 3D printing capability. These modifications will help to develop new capabilities for LLNL, including the printing of graded density materials, joints between differing material compositions, and materials with graded densities or microstructures. This involves development of controllable supply and mixing units for multiple material feedstocks, programming to generate multimaterial print files, write procedures for those programs, modification of commercial extrusion-based additive manufacturing printers, and integration of these capabilities into an automated graded, multimaterial 3D printing capability such that multiple material feedstocks can be supplied, mixed, and extruded through the 3D printer. The student will develop procedures for generating multimaterial print files (e.g., writing software for .gcode or .stl file generation, utilizing commercial software). They will learn how .gcode and .stl files are written and generated for additive manufacturing. The student will be familiarized with extrusion-based additive manufacturing principles, such as feedstock engineering (e.g., rheology, homogenization, extrusion methods). The student will utilize both computer-based and hands-on laboratory experience to potentially develop their software writing skills and utilize a commercial 3D printer. This project involves the ability to act on the world (supply, mix, print), a series of complex actions acting in coordination to perform a complex task; and the ability to communicate and coordinate with humans and others, human/machine interfaces that support the actions of the overall automated system.The student will be part of an interdisciplinary team. To test and facilitate steps in this research (e.g., supply, mixing, printing) and test how well the final graded, multimaterial 3D printed, the student may utilize equipment such as a scanning electron microscope, a thermogravimetric analyzer, a differential scanning calorimeter, a rheometer, and various furnaces and ovens. Students may also research additional emerging technological areas with potentially different technical goals and include processing from powders of ceramics and metals. Students may attend seminars in ES&H, MED, Materials and Characterization Institute (MaCI). Students may participate in student events and tours of LLNL's Advanced Manufacturing Laboratory as well as other facilities. Hosting Site:Lawrence Livermore National Laboratory Internship location: Livermore, CA Mentor:
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| Yes | NREL-Muthumanickam1 | 12/21/2022 | 1671598800000 | National Renewable Energy Laboratory | Golden, CO or virtual |
U.S. Citizenship is a requirement for this internship Project Description:The Industrialized Construction Innovation (ICI) team at NREL has been working with multiple industry partners specializing in offsite modular construction in factories and onsite automated construction such as 3D Printed buildings to support delivering quality-controlled energy efficient buildings at scale and speed. The ICI team is assisting these partners to explore strategies to utilize teleoperated and/or autonomous robotic arms to integrate thermal, mechanical, electrical, and plumbing systems in factory built modular building assemblies (offsite) and 3D printed buildings (onsite). The team has access to a 6-axis collaborative robotic arm (COBOT) (with a gripper end-effector) and a 7-axis industrial grade robotic arm (with a variety of end-effectors) on a linear rail at NREL facilities in Golden, which we are planning to utilize for pilot studies such as picking and placing building components such as timber struts, windowpanes, mechanical, electrical, and plumbing systems (conduits/ducts/outlets) in specific positions within a building assembly, and automated inspection of building assemblies using programmable computer vision sensors for faults (tolerance errors/deformations that lead to infiltration/leakages in buildings etc.). We would like to request an intern who can assist in developing computational simulations of such robotic processes, generating G-Code for robotic tasks, programming telerobotic control of robot/end-effector to perform tasks at specified intervals and implementing the G-Code using the actual robot to test the process for any discrepancies. Knowledge of architectural/construction engineering with 3D modelling skills alongside robotics would be preferred. If available to work in person at NREL, the intern can get to handle the robotic arm. If remote internship, tasks shall be limited to developing simulations of robotic processes, with possibility to remotely control robotic arm when onsite personnel (potentially the mentor) is present near the robot at NREL.Hosting Site:National Renewable Energy Laboratory Internship location: Golden, CO or virtual Mentors:
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| Yes | ANL-Plathottam1 | 12/21/2022 | 1671598800000 | Argonne National Laboratory | Lemont, IL or virtual |
U.S. Citizenship is a requirement for this internship Project Description:In this project, the intern will develop a surrogate model for a solar photovoltaic distributed energy resource (PV-DER) using Deep Operator Networks (DeepONet). This project will support the Department of Energy, Office of Electricity’s, Advanced Grid Modeling program as well as support Office of Science’s focus on Scientific Machine learning. If successful the project will help reduce the solution time required for large-scale power grid simulations involving thousands of PV-DERs.The primary focus of our project is applying artificial intelligence (AI) and scientific machine learning (SciML) for developing high-performance models of inverter-based solar energy resources connected to the electric distribution system. One application of these models is to simulate power grids with high renewable energy penetration much faster than what is possible with existing first principle models. The internship project will focus on taking an existing first principle PV-DER model with its dynamics defined by a system of ordinary differential equations (ODEs) and using them to train a DeepONet model that approximates the PV-DER dynamics with a high degree of accuracy for different perturbations. TASKS Under the guidance of the PI, the student will perform the following tasks: • Review relevant literature on Scientific Machine Learning (SciML) with a focus on DeepONet. • Review state-of-the-art open-source packages (in Python or Julia) which can be used for SciML. • Design a workflow for using solutions from the existing first principle model to train a DeepONet model. • Implement the workflow and train a surrogate model for at least one type of PV-DER. • Maintain the code related to the project in a public repository (GitHub). STUDENT REQUIREMENTS • Computer Science, Electrical Engineering, or Mathematics background. • Proficiency in Python. • Familiarity with Machine learning or dynamic modeling using ODEs. • At least a 3.0 GPA on a 4.0 scale. • A desire to participate in a dynamic work environment with competing deadlines. • Ability to be highly motivated and self-starting. Hosting Site:Argonne National Laboratory Internship location: Lemont, IL or virtual Mentor:
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| Yes | ORNL-Feldhausen1 | 12/21/2022 | 1671598800000 | Oak Ridge National Laboratory | Oak Ridge |
U.S. Citizenship is a requirement for this internship Project Description:This project is aimed at experimental studies of heat management during the hybrid (Laser-Wire Additive and 5-axis Subtractive) manufacturing process. The project includes experimental design, CAD Modeling, CAM Programming, and Implementation on the available hardware. Learning objectives for the applicant include: (i) develop a basic understanding of hybrid manufacturing; (ii) acquire skills in CNC machining and CAM programming additive operations; and (iii) gain experience in hybrid manufacturing via hands-on experiments in ORNL’s Manufacturing Demonstration Facility. Hosting Site:Oak Ridge National Laboratory Internship location: Oak Ridge Mentor:
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| Yes | SLAC-Thayer1 | 12/21/2022 | 1671598800000 | SLAC National Accelerator Laboratory | Menlo Park, CA |
U.S. Citizenship is a requirement for this internship Project Description:SLAC National Accelerator Laboratory is seeking an intern for the continued development of its Remotely Operated Accelerator Monitor (ROAM) robot. Currently, SLAC's accelerator facilities each rely on a network of fixed-placement sensors for supporting beam operations. The aim of ROAM is to augment the capabilities of operators by providing an ad-hoc mobile sensor platform that can be deployed as needed. The ROAM robot is not autonomous, and therefore requires piloting remotely by an operator from a safe location. The goal of this internship is to develop a dashboard that the remote operator can use to pilot the ROAM robot and view sensor information. Additionally, the intern will engage in the development of remote navigation tools to assist operator piloting and simulation environments for testing. Experience using Python, C++, and ROS is required.Hosting Site:SLAC National Accelerator Laboratory Internship location: Menlo Park, CA Mentor:
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| Yes | ANL-Park1 | 12/21/2022 | 1671598800000 | Argonne National Laboratory | Lemont, IL or virtual |
U.S. Citizenship is a requirement for this internship Project Description:Hot cells and glove boxes are widely used for manually handling radioactive and hazardous materials in nuclear and scientific applications. However, these systems require substantially expensive installations, human in close vicinity of the hazards, and have limited manipulation capability. There is a significant potential for enhancements in remote handling capability by introducing the recent advances in robotic systems and digital technologies. At Argonne, a robotic hot box (hot cell & glove box) system is being developed, which incorporates a dual-arm collaborative robot system and portable/modular glove box frame for scientific applications. In this student internship program, the student will be responsible for developing an enhanced remote operation method for the robotic hot box system based on multi-modal (visual-haptic) sensory feedback and action. The development will encompass implementation of extended-reality system via integration of augmented-reality (e.g. Oculus or Hololens) and haptic device (e.g. HaptX glove, or Phantom), and connection to motion operation in virtual and physical operation. The development will also utilize the 3D printing, and Argonne’s software infrastructure based on robot operating system (ROS) in the lab.Hosting Site:Argonne National Laboratory Internship location: Lemont, IL or virtual Mentors:
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| Yes | ANL-Park2 | 12/21/2022 | 1671598800000 | Argonne National Laboratory | Lemont, IL or virtual |
U.S. Citizenship is a requirement for this internship Project Description:At Argonne National Laboratory, an accelerator facility is used for production of medical radioisotope. Currently retrieval of the radioisotope materials in the target is done by human workers, exposing him to potential radiation hazards. To save the human workers from the hazardous environment in this task, Argonne robotics group is developing a mobile robot system that can be deployed into the accelerator facility and assemble and disassemble the target component from the beamline. In this summer internship project, the student will be tasked to develop robot operations program under various levels of remote automation. This task will involve both simulation and hardware implementation. Student with proficiency in programming (python and C++), simulation, CAD, and robot operating system will be suitable for the task.Hosting Site:Argonne National Laboratory Internship location: Lemont, IL or virtual Mentors:
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| Yes | ANL-Park3 | 12/21/2022 | 1671598800000 | Argonne National Laboratory | Lemont, IL or virtual |
U.S. Citizenship is a requirement for this internship Project Description:Materials are an essential element in the future of advanced energy technology and industry. However, the discovery of new materials is a lengthy and costly process, which also requires the knowledge and experience of highly trained experts. However, development of new materials is a lengthy and costly process, which also requires expert help. To this end, there is a great opportunity to scale-up and speed up the materials discovery process by incorporating robotics and artificial intelligence (AI). Such robotics systems are expected to bring about enhancements in all phases of next generation material discovery processes- process design, synthesis, characterization, and feedback optimization. However, the current state of the scientific robotic systems lacks the performance and flexibility to accommodate leading edge technologies for next generation materials discovery process. To this end, this project addresses the development of a new mobile robotic scientist platform that can simulate and execute automation and autonomous operations of materials discovery process. The development will entail integration Argonne’s technology basis on 3D virtual-reality simulation, sensor-based augmented-reality, teleoperation, and machine learning for the development of a digital robotics platform that facilitates hardware-in-the-loop simulation of experimental operations in a battery materials synthesis laboratory. The project task will entail both hardware and software development.Hosting Site:Argonne National Laboratory Internship location: Lemont, IL or virtual Mentors:
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| Yes | ANL-Park4 | 12/21/2022 | 1671598800000 | Argonne National Laboratory | Lemont, IL or virtual |
U.S. Citizenship is a requirement for this internship Project Description:With the recent emergence of electric vehicles (EV), recycling of EV battery is becoming a serious liability and opportunity in the future industry and circular economy. In particular, there is an opportunity for robotic application for disassembly of battery packs. Since battery packs are heavy, come in a large variety, and often involve high voltage, it is expected that robotic automation can provide transformative benefits to replace current manual processes to achieve enhancements in efficiency, safety, cost, and adaptability. To address the potential needs, the objective of the project is to develop a new telerobotic operation method, namely augmented teleautonomy, which enhances the efficiency and accuracy by integrating sensor based augmented reality and autonomous motion primitives via machine learning. In teleoperation, human operator operates a robot from a distance using visual-haptic human-robot interfaces. Augmented reality - the technology that can blend artificial entity with real world perception - can provide effective perceptual guidance to human operator in such operation. In addition, recent advances in machine learning technologies allow realization of complex and flexible autonomous robotic behaviors to adapt to the wide variety of battery types and associated processes. The development will be built on previous development of 3D simulation, sensing and augmented-reality display technology basis, and to integrate onto the remote control system of a collaborative two-arm robot system, for demonstration. The software development will be based on utilization of Robot Operating System (ROS) which is a Linux-based open-source distributed operating system.Hosting Site:Argonne National Laboratory Internship location: Lemont, IL or virtual Mentors:
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| Yes | ORNL-Nycz1 | 01/12/2023 | 1673499600000 | Oak Ridge National Laboratory | Oak Ridge, TN or Virtual |
U.S. Citizenship is a requirement for this internship Project Description:Wire arc additive technology at ORNL’MDF is a integration of advanced robotics, automation and classic GMAW (gas metal arc welding). The three arm Medusa system is an example of that approach. This project aims to create a prototype system capable of creating a functionally graded metal deposit. The student role will be to assist in the mechanical, electrical, and robotic design of the new system as well as perform the testing and results analysis.Hosting Site:Oak Ridge National Laboratory Internship location: Oak Ridge, TN or Virtual Mentors:
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| Yes | NREL-Boren1 | 01/12/2023 | 1673499600000 | National Renewable Energy Laboratory | Golden, CO or Virtual |
U.S. Citizenship is a requirement for this internship Project Description:Ocean wave energy could be harvested and converted into electricity via origami structures made from Distributed Embedded Energy Converter Technologies (DEEC-Tec). DEEC-Tec is centered upon the creation of material-like frameworks (think sheets of material) by way of interconnecting many energy transducers together. These material frameworks could, in turn, be folded, creased, and/or corrugated - this being origami mechanics - to construct overall ocean wave energy harvesting and converting structures.Motivating such ocean wave energy converters includes the following potential advantages: (i) ease of collapsibility - for transport, for deployment, and for surviving large storms and (ii) robust redundancy enabled by the combination of many energy transducers that convert ocean wave energy throughout structure rather than concentrating it into a central generator. Hosting Site:National Renewable Energy Laboratory Internship location: Golden, CO or Virtual Mentors:
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| Yes | ANL-Stutenberg1 | 01/12/2023 | 1673499600000 | Argonne National Laboratory | Batavia, IL or Virtual |
U.S. Citizenship is a requirement for this internship Project Description:The goal of this project will be to support the development and demonstration of a system for capturing in-field data from connected and automated vehicle technologies. The participant will gain direct experience with development of core components of the system, refining the system through experimentation, and integrating the system with laboratory experiments. The participant will gain hands on experience working with advanced mobility technologies at a vehicle systems level, developing a deep understanding of how these systems work and gaining direct experience utilizing open source software and advanced machine learning / computer vision techniques. Hosting Site:Argonne National Laboratory Internship location: Batavia, IL or Virtual Mentor:
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| Yes | ORNL-Meyer1 | 01/12/2023 | 1673499600000 | Oak Ridge National Laboratory | Oak Ridge, TN |
U.S. Citizenship is a requirement for this internship Project Description:The selected participant will work on engineering design for an Autonomous Chemistry Lab, including research on development on pick-and-place routines for both a robotic arm attached to a mobile platform. The project will have a strong focus on all aspects of engineering design, from CAD and programming robot controls to manufacturing prototype components and routines to iterative testing and validation with physical hardware. The participant will work with engineers to improve gripper designs and create efficient, reliable methods to accomplish pre-defined chemistry tasks within a multi-purpose laboratory. The participant will also participate and engage in meetings with other ORNL staff and industry partners. Hosting Site:Oak Ridge National Laboratory Internship location: Oak Ridge, TN Mentor:
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| Yes | ORNL-Carter1 | 01/12/2023 | 1673499600000 | Oak Ridge National Laboratory | Oak Ridge, TN |
U.S. Citizenship is a requirement for this internship Project Description:Participant will research and develop various metal Additive Manufacturing (AM) robotics systems. Specifically, he or she will help develop a framework for monitoring, sensing, and data acquisition for Metal Big Area Additive Manufacturing (mBAAM) with focus on thermal history. In addition to developing a new AM monitoring and data acquisition system, the candidate will become very familiar with mBAAM and perform extensive research. He or she will interact with our corporate partners and industry leaders to help design and develop new generations of mBAAM systems. He or she will also explore avenues for implementing closed loop control for improved thermal management and part quality. Lastly, some research will involve robotic software script programming for various calibration exercises. Hosting Site:Oak Ridge National Laboratory Internship location: Oak Ridge, TN Mentors:
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| Yes | NREL-Nichols1 | 01/18/2023 | 1674018000000 | National Renewable Energy Laboratory | Golden, CO or Virtual |
U.S. Citizenship is a requirement for this internship Project Description:Commercial wind blades continue to get longer every year with offshore wind blades now exceeding 100 meters in length. There are major opportunities in improving the manufacturing of wind blades by improving worker safety, blade geometry repeatability, and overall cost.The NREL team leads a research effort to make these process improvements and validates new methods with the Kuka industrial robot platform in the CoMET. This system is a 7-axis advanced industrial robot with a 300-kg payload rating, 2.5-meters arm reach, and a track a 6.6 meter stroke. This platform functions as a tool for the team of researchers to enable the research and development of automation hardware and software that will scale for the manufacturing of large offshore wind blades. The robotics to be developed through this program will be capable of safely working at the heights necessary in a blade factory for manufacturing large offshore wind blades while keeping operators safely on the ground. The NREL team is seeking a EERE Robotics Intern to assist in these research efforts. The intern will gain experience designing, implementing, and optimizing industrial robot modeling, software and hardware to achieve the desired results in blade safety, reliability and cost. This may include opportunities exploring: • Design of Gazebo Robotics Simulation Environments • AGV path programming with ROS (c++ and python) and Gazebo • Computer vision to develop innovative path planning algorithms • Machine learning to improve robot toolpath generation quality • Robot mobilization within blade manufacturing facilities • Laboratory testing of blade manufacturing technologies Hosting Site:National Renewable Energy Laboratory Internship location: Golden, CO or Virtual Mentor:
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The name and contact information of the hosting site internship coordinator is provided for further assistance with questions regarding the hosting site; local housing availability, cost, or roommates; local transportation; security clearance requirements; internship start and end dates; and other administrative issues specific to that research facility. If you contact the internship coordinator, identify yourself as an applicant to the NSF Mathematical Sciences Graduate Internship (MSGI) Program.
Interns will not enter into an employee/employer relationship with the Hosting Site, ORAU/ORISE, EERE or DOE. No commitment with regard to later employment is implied or should be inferred.