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Electrical Engineering / Mechatronics / Automation Engineering / Computer Science or related fields Interest in battery technologies and electromobility Basic knowledge of electrochemistry or willingness
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data to modeling in digital representations to the final visualization and human-machine interaction. We develop modern AI solutions in the areas of 2D/3D image and video understanding, computer vision
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transfer. Its current five-year scientific programme, Molecules to Ecosystems (2022–2026), aims to deepen our understanding of life — from molecular mechanisms to complex ecosystems — with a strong emphasis
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, the fertilization process. Our research focuses on the physiology and pharmacology of ion channels, employing cutting-edge techniques such as electrophysiology, advanced fluorometric imaging (Ca²⁺, pH, membrane
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novel machine learning-guided approaches. The position is located at TUM Campus Heilbronn. Your qualifications Strong background in computer science, AI, or related areas or similar fields. Solid
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or Mechanical Engineering – strong background in machine learning, deep learning and / or computer vision – good programming skills in Python (and C++) – basic knowledge of optics including concepts like PSF, MTF
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. To achieve this, we create a rich AI-training dataset for multi-modal inferences, combining computer-vision, environmental parameter measures and DNA data. Your Tasks Participation in fieldwork in Germany
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particular emphasis on the Baltic Sea. Scientists at the IOW work across five departments—Physical Oceanography, Marine Chemistry, Biological Oceanography, Marine Geosciences, and Marine Observations—in
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this knowledge gap and establish improved GHG models accounting for soil invertebrates. To achieve this, we create a rich AI-training dataset for multi-modal inferences, combining computer-vision, environmental
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completed your doctorate in industrial ecology or energy systems analysis You hold a very good degree (master`s or equivalent) in engineering (e.g., energy technology, mechanical engineering, process