77 data-"https:" "https:" "https:" "https:" "https:" "Simons Foundation" scholarships at Nature Careers
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omics data. This interdisciplinary atmosphere has been a main catalyst for many past successes: https://europepmc.org/article/MED/35021063 , https://europepmc.org/article/MED/31819264 , https
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allowance. The employment will initially be limited to three years. Important: Applicants must not have resided in Germany for more than 12 months in the past three years. Group website: https://biophys.uni
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. For additional information, please visit: https://dakic.univie.ac.at/ . Your future tasks: You will actively participate in research, teaching and administration. This means: • You are involved in a well-funded
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of young scientists (Master / PhD / Postdoc). Our expertise lies in quantum foundations, quantum information theory and quantum technologies. For additional information, please visit: https
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breast cancer patients, explore the activation mechanism of antiviral defense pathways in cell cultures and perform bioinformatics analysis of data. The work will be performed in a modern molecular biology
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of novel probabilistic deep-learning models that automatically extract mechanistic and statistical knowledge from your in vivo perturbational omics data. This interdisciplinary atmosphere has been a main
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collect and analyse health data, identify risks, advise government and experts, and develop new scientific methods. We are based in Berlin, Wildau and Wernigerode. Get started now Apply directly through the
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at the Institute of Medical Informatics within the research group “Medical Data Integration Center (MeDIC)” led by Dr. Michael Storck and is embedded in the DFG-funded Collaborative Research Centre 1748, Principles
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learning Distributed and federated training The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another
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Learning with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains