223 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" Postdoctoral positions in Germany
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. If you do not yet have a transcript evaluation, please note that you may have to request one if your application is successful. For further information, please visit the website: https://www.kmk.org/zab
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projects within the Clusters of Excellence ‘Machine Learning for Science’ and ‘Image-Guided and Functionally Instructed Tumor Therapies (iFIT)’. Requirements PhD in Bioinformatics, Computational Biology, or
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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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multimodal vision-language models for prompt-based 3D medical image segmentation Work with large-scale clinical CT datasets and scalable deep learning pipelines Validate models in close collaboration with
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: analysis of probabilistic systems (Markov decision processes, stochastic games, chemical reaction networks), automata theory and temporal logic, machine learning in verification, building model checkers
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machine learning algorithms Strong communication skills and ability to work in interdisciplinary teams Fluency in spoken and written English We offer: A dynamic and interactive research environment as
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: https://www.pisa.tum.de/pisa/jobs/ Für eine der PISA Hauptdomänen (Lesen, Mathematik, Naturwissenschaften) nach Wahl wird zum nächstmöglichen Zeitpunkt ein*e Postdoc gesucht. Nähere Informationen zur
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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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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | about 1 month 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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is expected. Background Information: Nat. Chem. 2015, 7, 105; Chem. Eur. J. 2019, 25, 4590; Angew. Chem. Int. Ed. 2019, 58, 418; Nanoscale 2021, 13, 19884. www.sengegroup.eu https://www.ias.tum.de