48 parallel-processing-bioinformatics Postdoctoral positions at Delft University of Technology (TU Delft) in Netherlands
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, design, production including application and product improvement, materials, processes and (mechanical) systems. ME is a dynamic and innovative faculty with high-tech lab facilities and international reach
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Contribute your computer vision
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techniques with the constraints of silicon processes. In parallel, you will develop on-chip antenna structures, performing full-wave EM simulations and translating these designs into manufacturable layouts
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position, you will lead the development of a probabilistic, error-aware surrogate model capable of delivering fast, uncertainty-quantified predictions for complex multiscale–multiphysics processes in OFPV
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process. This position offers a unique opportunity to combine educational research and innovation. Over the past years, our research team has developed a shared conceptual and empirical foundation
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the Netherlands. Additional information If you would like more information about this vacancy or the selection procedure, please contact Lotte Asveld, L.Asveld@tudelft.nl . Application procedure Are you interested
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., Cu, Al, Fe) and for better recovery of Co, Ni, and Mn. Identify and optimize key process parameters for black mass treatment in close collaboration with industry partners. Background Batteries
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-structure interaction of the Maeslant Barrier, combining field modal testing, numerical modelling, and signal processing to capture and interpret its dynamic response under current and future hydraulic and
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Materials Science and Engineering, Applied Physics, Chemical Engineering, or Electrical Engineering. Technical Expertise: Demonstrated and extensive experience in materials processing, surface analysis, and
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will image them using a variety of microscopy methods, and collaborate with a team of computer vision scientists to build ML-based models for phenotype prediction, helping to accelerate the cell