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<span lang="en">TUD Dresden University of Technology</span> | Dresden, Sachsen | Germany | about 5 hours ago
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At the Faculty of Electrical and Computer Engineering, Institute of Circuits and Systems, the Chair of Highly-Parallel VLSI Systems and Neuro
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research in leading journals and conferences. Attract competitive research funding in Denmark and Europe. Teach and develop courses in parallel computer systems, parallel programming, and HPC, and supervise
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optimisation Experience in one or more of the following areas would be advantageous: EO or geospatial data analysis, parallel or distributed computing, GPU programming, Linux and containerised
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, Mechanical or Electrical Engineering, Control, Applied Mathematics, or a related field. You should have a solid foundation in machine learning and strong programming skills (Python, deep learning
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, computational science, or closely related). Relevant coursework (examples): Computational physics/scientific computing and numerical methods for PDEs High‑performance computing (parallel distributed
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and training in GPU programming, data science applications, and scientific computing workflows for the SciNet advanced computing consortium. The incumbent supports users in designing, training, and
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[Eshmuminov et al. Nat. Biotechnol. 2020] and the first clinical transplantation of a liver treated and recovered in a perfusion machine [Clavien et al. Nat. Biotechnol. 2022]. In parallel, the lab has
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coursework (examples): Computational physics/scientific computing and numerical methods for PDEs High‑performance computing (parallel distributed programming, GPU programming) Astrophysical fluid dynamics
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sufficient. Assigning meaning to prototypes is however a time-consuming task, as the various models tend to learn prototypes that do not carry sufficiently high-level meaning. A parallel line of work
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programming skills, and prior experience training and evaluating neural network models (e.g. in PyTorch). Familiarity with biomechanics or finite-element modelling is an advantage. Background