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and Applications, R Language for Statistics, Data Mining with Big Data, Artificial Intelligence and Deep Learning, Statistical Reasoning for Business Decisions, Data Visualization; Highly Desired: Text
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disciplines associated with geography, soil sciences, hydrology, civil engineering, or related discipline, with research expertise in geospatial AI, deep learning foundation models, hydrology, river science
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the world’s largest supercomputers (Polaris, Aurora) and some of the most advanced characterization tools in the world at Argonne and Sandia National Labs. Candidates with a background in deep learning
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The AITHYRA-CeMM Joint International PhD Call in Molecular Medicine and Artificial Intelligence (m/f
-20 fully funded PhD positions here: https://apply.cemm.at/ Supported by the Medical University of Vienna, the Technical University of Vienna and University of Vienna, the AITHYRA and CeMM PhD programs
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– Resolution of appeals 16.01.2026 – 4:00 PM – Final results Additional comments We are seeking a PhD student in Machine and Deep Learning for Satellite Image Processing to join our team, who will be
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ready to travel Applicants must be eligible to enroll on a PhD program at TU Dresden (see https://tu-dresden.de/ing/maschinenwesen/postgraduales/promotion?set_language=en ) Eligibility requirements
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, or a related field. Proven experience in machine learning, deep learning, generative AI and data mining. Strong programming skills (e.g., Python, R, MATLAB, or similar). Experience with data
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willing to learn from other people in the lab and collaborate. Candidates should have a master’s degree in Molecular biology, Biochemistry, or similar field and have deep interest in molecular biology and
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of the host organization of last 36 months. As secondments and events are foreseen, applicants must be ready to travel Applicants must be eligible to enroll on a PhD program at TU Dresden (see https://tu
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conducting experiments for training and evaluating deep neural networks Knowledge of multi-modal learning, transfer learning, transformers, or self-supervised learning Experience in dealing with large medical