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engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Germany Application Deadline 15 Mar 2025 - 23:59 (Europe/Berlin) Type of Contract Temporary Job Status Full-time Hours
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for improved understanding of structural and kinetic processes in electrolytes; and machine learning concepts for improved analysis of experimental and simulated data. Material Synthesis Within this research
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CMFI Cluster of Excellence iFIT Cluster of Excellence Machine Learning CIN LEAD Graduate School & Research Network Collaborative Research Centers Transregional Collaborative Research Centers (CRC-TRRs
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different sources (observation, model output) You are interested in applying methods for automated detections of weather (e.g., cyclones, jets, frontal systems) which can include machine learning methods You
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Description PhD Position at KIT Graduate School for Climate and Environment (GRACE) The Graduate School for Climate and Environment (GRACE) at Karlsruhe Institute of Technology (KIT) invites
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. Ideally you have Programming skills and knowledge on machine learning and statistical data evaluation, creation of scientific programme codes using common software packages (MATLAB, Python, R) Simulation
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. programming languages, tools, machine learning, Al etc. Excellent proficiency of English or German One PhD position must be filled by a researcher with at least a C1 language qualification in German due
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Max Planck Institute for Human Cognitive and Brain Sciences • | Leipzig, Sachsen | Germany | about 12 hours ago
the IMPRS CoNI is English, German language skills are not mandatory. For PhD applicants, a minimum score required for a TOEFL paper-based test is 550. The minimum score for the TOEFL computer-based test is
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Description PhD Position at KIT Graduate School for Climate and Environment (GRACE) The Graduate School for Climate and Environment (GRACE) at Karlsruhe Institute of Technology (KIT) invites
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and interpretable quality assessment algorithms. This research combines various machine learning topics, including uncertainty, explainability, and fairness in supervised and unsupervised deep learning