92 quantum-physics-"https:"-"https:"-"https:"-"M.V" Postdoctoral positions at Nature Careers
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optimize chemical compounds. Your work relies heavily on the ability to synthesize complex chemical structures and to investigate and understand chemical-physical and biological properties of chemical
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set for twelve months at a time, paid out on a six months basis. In exceptional cases, shorter periods may be acceptable. Application process An application must contain the following documents in
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members fostering a collaborative atmosphere (2 Prof., 6 PostDocs, 12 PhD students, 3 Research assistants). SpaceR's state-of-the-art facilities include two physical laboratories, the LunaLab and Zero-G Lab
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selection and switching in real time Integrate surrogate models with physics-based solvers, e.g. SOFA, FEniCSx, SOniCS, and clinical or phantom data Deploy models on ARSPECTRA hardware, including optimisation
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of Molecular Cell Biology and Genetics (MPI-CBG), the Max Planck Institute for the Physics of Complex Systems (MPI-PKS), and the Technische Universität Dresden (TUD), and a stimulating and inspiring atmosphere
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Postdoctoral Research Associate - Hybrid Computational-Experimental Scientist in Bacterial Drug Resp
in mentoring and shaping the lab's interdisciplinary culture. We Are Looking For Someone Who • Has a PhD in computational biology, microbiology, systems biology, engineering, physics, CS, or a related
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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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Package: Actively participate in a participant-driven co-design process to develop a framework for returning molecular and imaging data to study participants Contribute scientific content to patient
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of microorganisms, the development of fermentation processes and the engineering of microorganisms to process food, produce food ingredients, food additives or food processing aids. Aparticular focus is put
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scale DNA libraries with generative synthesis models, translating in silico predictions into physical gene libraries for experimental testing. Building and evaluating probabilistic deep learning models