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Field
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Physical Requirements Sitting at computer workstation for long periods of time; Standing for long periods of time; Repetitive motion; Job Related Travel (occasional); Hazardous Chemicals or fumes; Burns/Cuts
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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, concentration and functional inequalities • Mathematical aspects of machine learning and deep neural networks • Free Probability aspects of Quantum Information Theory. While excellent candidates with other
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Post Doctoral Researcher in Digital Twins CO2-to-Protein production in collaboration between the ...
collaboration between the Department of Electrical and Computer Engineering and the Novo Nordisk Foundation CO2 research center, Aarhus University, we aim to address this opportunity by developing digital twins
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including code design, documentation and testing. Familiarity with optimization methods including Machine Learning (ML) techniques. Any experience with computations on GPUs. Working knowledge of Linux command
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Python is required. Programming in C or C++ is a plus. Background in statistical genomics, longitudinal modeling, non-parametric statistics, machine learning and deep learning are preferred and encouraged
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first author publications in reputable peer-reviewed journals Advanced quantitative skills (e.g., advanced stats [MLM], machine learning, data mining). Willingness to develop desired skills (see directly
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conditions, and brain tissue microstructure and functioning. The successful candidate will be working within a multi-disciplinary team of MRI physicists, computer scientists, radiologists, neuroscientists, and
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skills Proficient with computer applications and programs associated with the position (i.e. Microsoft Office suite) Strong attention to detail and follow-up skills Strong customer service skills and phone
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, computational, and machine learning/AI methods, with a particular emphasis on deep learning approaches improve our understanding and prediction of infectious disease dynamics. Projects are also strongly grounded