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related discipline. Strong expertise in medical imaging and/or machine learning. Excellent programming and research skills. Interest in translational research and interdisciplinary collaboration with
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Senior Scientist / Group Leader on Bioinformatics / Computational Biology on RNA Regulation in Disea
, and validate computational findings Apply machine learning and statistical modeling techniques to identify patterns and predict functional impacts of RNA modifications Contribute to publications
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., experience sampling, virtual/mixed/extended reality, machine learning, artificial intelligence) are desirable. The professorship, integrated into the Chair of Personality and Educational Psychology, is
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. Strong coding skills for programming neural networks, machine learning and machine learning software frameworks (e.g. PyTorch or Jax) is a must. The ability for creative and analytical thinking across
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(specifically machine learning) in early drug discovery, precision medicine and clinical trials. The team is internationally well connected and is working closely together with leading European and US
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knowledge of quantitative methods, particularly in statistics and econometrics; experience in machine learning is a plus Background in business/management/behavioral science Experience with programming
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well as an active engagement in the 3RTG activities. Requirements: successfully completed university degree (Master's, Diploma or equivalent) and relevant PhD in Computer Science, Computer Engineering, or related
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domains are e.g., signal-/image processing, artificial intelligence and machine learning. Tasks: research and development in designing and programming field programmable gate arrays (FPGAs) for accelerating
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strengthen the data science and machine learning activities of the IAS-9 with exciting new topics. You will work in a multidisciplinary team of enthusiastic data scientists, software developers and domain
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performance in fuel cell (biogas) and co-electrolysis applications. To achieve this, you will employ computational fluid dynamics (CFD) and machine learning (ML) to investigate degradation mechanisms under