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September 2026) in Computer Science, Artificial Intelligence, Applied Mathematics, Hydrology, Civil Engineering, Actuarial Science or a related field; Strong programming skills (Python; familiarity with deep
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, physiology-aware models of bacteriophage infection by combining high-throughput single-cell microscopy, microfluidics, image analysis, and mathematical modelling. The work will contribute to understanding how
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engineering, control engineering, or a related discipline. A strong foundation in at least one of: electrochemical or energy-system modelling, dynamic modelling and control, mathematical optimization, or data
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integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time
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Energy For more information see: www.uu.se/earthsciences The successful candidate will join the research program Air, Water and Landscape Sciences (LUVAL) (https://www.uu.se/en/department/earth-sciences
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engineering Engineering » Other Mathematics » Applied mathematics Physics » Applied physics Physics » Other Researcher Profile First Stage Researcher (R1) Application Deadline 4 Oct 2026 - 21:59 (UTC) Country
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Infrastructure? No Offer Description The Cybersecurity (https://networks.imdea.org/es/equipo/grupos-de-investigacion/cybersecur… ) and Internet Analytics Groups (https://networks.imdea.org/es/equipo/grupos-de
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candidates hold a Master’s degree in Informatics, Mathematics, or a related field, and possess strong expertise in linear algebra, GPU architectures, and programming in C++ and Python. This is a 100% TVL E13
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Apply online now: https://karriere.klinikum.uni-heidelberg.de/index.php?ac=application&jobad_id=28417 PhD student (m/f/d) with a focus on epidemiology and tools for screening of Tuberculosis
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for recruitment positions for general criteria for the position. Preferred selection criteria Experience with computer vision, video analysis, self-supervised learning, vision transformers, or multimodal learning