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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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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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-Phenomenology (hep-ph) , High Energy Physics , High Energy Theory , Machine Learning , Neutrino physics , Particle , Particle Physics , Particle Theory , QCD , Theoretical high energy physics , Theoretical High
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) analysis • Research, development and implementation of deep-learning approaches • Network architecture search • Real-time image analysis • Establishing multi modal (video, thermography, acoustic, RFID
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 22 days ago
deep learning (x/f/d/m) Background With the project Deepcloud, we will leverage the machine-learning revolution to understand clouds and their role in the climate system. We aim to train a deep learning
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 7 days 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
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with