21 density-functional-theory-postdoc Postdoctoral positions at Delft University of Technology (TU Delft) in Netherlands
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/354748/postdoc-researcher/apply/ Requirements Additional Information Website for additional job details https://www.academictransfer.com/354748/ Work Location(s) Number of offers available2Company
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information for dexterous manipulation. Your role and impact as postdoctoral researcher, you'll lead the research in leveraging our tactile sensor technology to control grippers and multi-finger hands. Your
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This Postdoc position focuses on the development of assessment and
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breakthrough for gas-free heating in high-rise and stacked housing, where roof space is limited and noise restrictions apply. As postdoc, you will take a leading role in the modeling and validation
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& utilization technologies, we are looking for a proactive, process engineering-minded postdoc to join our team. We expect both scientific and engineering work, that will focus on first classifying separation
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at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine
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, and will play a key role in linking experiments with simulations from a companion PhD project. The work will involve close collaboration with industrial partners and is expected to provide both
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Description Shape the future of robotics! TU Delft is looking for a postdoc to develop a design framework with guidelines, competences, and tools that will help robots integrate into professional work settings
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looking for a highly motivated PostDoc candidate who wants to work on efficient and reliable digital CIM-based AI accelerator design for future neuromorphic accelerators. The candidate will work on the
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Engineering (QCE) department is looking for a highly motivated PostDoc candidate who wants to work on efficient and reliable analog CIM-based AI accelerator design for future neuromorphic accelerators