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- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); Delft
- Delft University of Technology (TU Delft); yesterday published
- Utrecht University
- Delft University of Technology (TU Delft); 16 Oct ’25 published
- Eindhoven University of Technology (TU/e)
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- Eindhoven University of Technology (TU/e); Eindhoven
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- Maastricht University (UM); yesterday published
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to outstanding international research and education in the field of civil engineering, applied earth sciences, traffic and transport, water technology, and delta technology. Our research feeds into our educational
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engineering, applied earth sciences, traffic and transport, water technology, and delta technology. Our research feeds into our educational programmes and covers societal challenges such as climate change
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developers, electrical and mechanical engineers. Experience and strong understanding of machine learning algorithms, mathematical modelling, and applications of AI. Proficiency in Python, leading ML frameworks
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within a cross-functional team, including software developers, electrical and mechanical engineers. Experience and strong understanding of machine learning algorithms, mathematical modelling, and
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international research and education in the field of civil engineering, applied earth sciences, traffic and transport, water technology, and delta technology. Our research feeds into our educational programmes
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the bottom up and study these in biomimetic solid-state nanopores. The approach can be expected to uncover the vital mechanism of selective IDP-mediated transport and thus answer one of the central questions
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not rely purely on common research metrics. The successful applicant will have: PhD degree in Mechanical Engineering, Energy Engineering, Applied Physics, Electrical Engineering, or a related field
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the reliability failure mechanisms of digital CIM architectures. Developing solutions to address the reliability issues. The PostDoc position is part of the CONVOLVE, a multi-partner European project involving
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CIM accelerator Develop efficient mapping schemes of AI workloads Identifying the reliability failure mechanisms of digital CIM architectures. Developing solutions to address the reliability issues
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using TEM, AFM, FRET, DIB, MINFLUX, etc. We study purified yeast NPCs as well as design the amino-acid sequence of the IDPs and TRs de novo from the bottom up and study these in biomimetic solid-state