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eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more about these options, please contact the NRC
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connections to their communities and you may be eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more
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, and machine learning methods, choosing the approach that best fits the scientific question. Investigate systematically what information is contained in imaging data, how it can be extracted, and how
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This PhD project, part of the REACT MSCA Doctoral Network, aims to develop an energy-efficient compute-in-memory (CIM) architecture using gain-cell memory for real-time edge learning, addressing
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motivated students with a strong background in engineering or computer science. The ideal candidate will have: Strong programming and software skills. An awareness of machine learning theory and techniques
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applicants with a background or experience in: • Computational chemistry • Materials simulation • Scientific machine learning / AI • Molecular dynamics or DFT • Materials science or a related discipline
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: experience in (stem) cell handling and culture experience with molecular techniques willingness to work with animal models (including pigs) and tissue fluent user of computer software (e.g. Microsoft Office
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and machine-learning methods for multi-objective optimization of efficiency, reproducibility, and operational stability Study intrinsic material stability, light-induced phase segregation, ion migration
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assurance for embedded systems by combining advanced side-channel analysis, fault-injection techniques, AI- and machine-learning-assisted analysis, robustness evaluation, and quantitative security assessment
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connections to their communities and you may be eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more