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/fractionalized phases of matter, via computational many-body physics nonequilibrium quantum dynamics, to quantum computation, quantum information, and machine learning. The Institute provides a stimulating
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-body physics nonequilibrium quantum dynamics, to quantum computation, quantum information, and machine learning. The Institute provides a stimulating environment due to an active in-house workshop
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Oxford Nanopore Technologies (ONT). Your role will be central in creating and applying bioinformatics and machine learning tools to analyze long-read data and decipher cap-specific signals from raw
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Your Job: As part of an interdisciplinary project team with researchers from bioinformatics you will work on quantum algorithms for drug discovery. Here, the focus lies on machine learning and
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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qualifications: PhD or equivalent achievement (proof of independent research capability) in Machine Learning, Computer Science, Physics, Mathematics, or a related field Deep theoretical knowledge and extensive
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and others) Analysis of the experimental data, ideally connecting to our machine learning tools Presentation of scientific results on conferences and in publications Requirements PhD degree in physics
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to the position must hold a doctoral degree in social or behavioral sciences (incl. human-computer interactions with relevant experience). Applicants must demonstrate experience in experimental work with human
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European research consortia such as the DAPHNE (DAta for PHoton and Neutron Experiments) NFDI consortium and the Cluster of Excellence "Machine Learning: New Perspectives for Science". Details