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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 1 month ago
Job description:Postdoc (f/m/d) AI-based automated adaptation and biological optimization of proton therapy With cutting-edge research in the fields of ENERGY, HEALTH and MATTER, around 1,500 employees
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13.10.2025, Wissenschaftliches Personal The research group Cyber-Physical Systems of Prof. Matthias Althoff at the Technical University of Munich offers a PhD/Postdoc position in the area of
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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Your Job: Develop and refine detailed models of power electronics and their control systems for stationary battery storage applications Focus on system identification and parameter optimization
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to the successful implementation of interdisciplinary initiatives. Requirements We are looking for a researcher (m/f/d) with: Completed PhD: You hold a PhD in stem cell biology, molecular and cell biology, biomedical
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description: You will have the following tasks: Development and optimization of micromanipulators and automated systems for microbiopsy together with the PhD student Integration of microbiopsy system into soft
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-Technology-Co-Optimization: STCO), AI-driven data analysis and process assessment, packaging-specific PDKs & ADKs, or inline testing & fault analysis (3D)? If so, you are in the right place! What you will do
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: Completed PhD: You hold a PhD in stem cell biology, molecular and cell biology, biomedical engineering, or a related discipline, with a clear and demonstrated focus on diabetes research. Experience with iPSC
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epithelial cell biology, cellular metabolism and mitochondrial function. Experience in mouse models and ex vivo organoid culture is a prerequisite. The candidate should also have the ambition and commitment
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under small temperature gradients (ΔT ≤ 30 K). Design and Optimize novel fabrication techniques to achieve high packing densities in thermoelectric generators (TEGs) for powering IoT sensor networks