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Your profile PhD degree in Computer Science, Applied Mathematics, Computational Engineering, Physics, or an equivalent field with a focus on High-Performance Computing Advanced Know-How in the fields
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interdisciplinary, and together we contribute to science and society. Your role The selected candidate will develop a computational platform to identify hierarchical combinations of cell fate conversion factors
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information encoding and computation in neocortical circuits. The successful candidate will lead an ambitious project on synaptic and circuit mechanisms of sensory processing in primary visual cortex (V1
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also interdisciplinary knowledge on the subject. More precisely: PhD degree in computer science, machine learning, computational biology, or a closely related field Strong research track record
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SD-26085 POSTDOCTORAL RESEARCHER IN THE OXIDATIVE CHEMICAL VAPOR DEPOSITION OF FUNCTIONAL BUILDIN...
in December 2027). Is Your profile described below? Are you our future colleague? Apply now! Education · The candidate should have a PhD degree in Chemistry, Chemical Engineering, Materials
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) is an interdisciplinary research centre of the University of Luxembourg. We conduct fundamental and translational research in the field of Systems Biology and Biomedicine - in the lab, in the clinic
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) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission of teaching and
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the communication on the master programme in architecture Tutoring and evaluating master and PHD students Administrative tasks Your profile Master and Ph.D. degree in architecture or urbanism, or in
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consortium Your profile Required qualifications: PhD in neuroscience, stem cell biology, neurobiology, or a related field Strong experience with human iPSC culture and neural differentiation Solid background
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, computational mechanics, computer science, applied mathematics or similar Strong experience with deep learning, e.g. PyTorch, JAX, TensorFlow, and probabilistic methods Familiarity with graph neural networks