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on advanced machine learning and emulation approaches. Key responsibilities: The candidates will be expected to work on the following tasks: - Develop machine learning (ML) methodologies appropriate
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Molecular Biotechnology (IPMB) of Heidelberg University is offering a 3-year full-time (100%; 39,5 h/week) postdoc position in Multiomics Data Integration and Machine-Learning for therapy response prediction
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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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learning, and computer graphics. The positions are fully-funded with payments and benefits according to German public service positions (TV-L E13, 100% for PhDs and TV-L E14, 100% for PostDocs; 45k – 57k
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learning, and computer graphics. The positions are fully-funded with payments and benefits according to German public service positions (TV-L E13, 100% for PhDs and TV-L E14, 100% for PostDocs; 45k – 57k
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06.12.2021, Wissenschaftliches Personal The professorship of Data Science in Earth Observation is seeking six new PhD candidates/PostDocs for its new center for Machine Learning in Earth Observation
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, Dark Matter and Axion Physics , Elementary Particle Physics , hep , hep-ph , HEP-Phenomenology (hep-ph) , High Energy Physics , High Energy Theory , Machine Learning , Neutrino physics , Particle
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skills in Python, Java, C++, etc. A solid foundation in generative AI, machine learning, and related areas. An Interest in eye-tracking technology, Computer Vision, Speech/ Language Processing, VR, and AR
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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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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 1 day ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine