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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Oldenburg Oldenburg, Niedersachsen | Germany | 10 days ago
that provides equal opportunities. We are convinced that diverse teams and a variety of perspectives enrich our work and our daily collaboration. In a continuous process of learning and reflection, we aim
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23.02.2026 Application deadline : 30.04.2026 The Autonomous Systems Lab at the University of Tübingen is searching for a Postdoctoral Researcher in machine learning (m/f/d, E13 TV-L, 100%) limited
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support, utilizing pharmacokinetics database PK-DB and individualized patient data (more information: https://livermetabolism.com/atlas ) Requirements: - completed scientific university degree in (bio
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processes, in order to explain their long-term developments and to understand how they resonate into the present (https://www.uni-kiel.de/en/cluster-roots ). ROOTS combines expertise from a wide array of
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partners. The postdoctoral researcher will also contribute to teaching in areas such as Machine Learning, NLP, AI for Education, Explainable AI, and Python-based applied seminars, supporting course
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Research FieldEngineering » Materials engineeringEducation LevelPhD or equivalent Skills/Qualifications To acquire further scientific qualification (habilitation), the job holder should be able to cover a
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workflows within SolMates development of scalable, cloud-based workflows, including the integration of machine learning components into Temporal-based workflow orchestration for data processing, analysis, and
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++, Python, and JavaScript languages, multi- and many-core SoC, RISC-V, hardware synthesis, hardware-software co-design, (meta-heuristic) optimization algorithms, machine learning frameworks, (bonus topics
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to industry partners to learn and set up on-site research Preparing scientific publications and project reports Your Profile: Strong motivation for an interdisciplinary project that combines physics and biology
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socio-economic analysis, equipping them with advanced skills in reservoir modelling, machine learning, advanced oxidation processes, and microbial enhanced recovery. Candidates will also develop intuitive