230 machine-learning "https:" "https:" "https:" "https:" "https:" positions in Germany
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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
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methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team orientation excellent
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plate array microscope for simultaneous time-lapse video microscopy, enabling high-throughput single-cell analyses of rapidly migrating cells. You will be responsible for Develop new machine learning
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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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research areas. New insights and synergetic effects resulting from collaboration between inherently different viewpoints of separate fields typically accompany this endeavour. Our task force on machine
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interdisciplinary team. Tasks: Assembly and annotation of phytoplankton genomes and metagenomes Further development of bioinformatic data analysis pipelines Use and further development of deep learning tools
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 28 days ago
applicant has a strong background in bioinformatics and/or probabilistic machine learning, as well as experience in omics data analysis, and possesses solid English-language skills. Experience with
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Deutsches Zentrum für Neurodegenerative Erkrankungen | Bonn, Nordrhein Westfalen | Germany | about 1 month ago
programming, machine learning (scikit-learn, PyTorch/TensorFlow), ontologies (OWL/RDF), and/or computational cognitive modelling Interest in interdisciplinary research at the interface of computer science
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strong AI focus; a PhD is desirable, but not mandatory Advanced knowledge of machine learning, statistical modeling, and modern AI methodologies Strong programming skills, preferably in Python and common