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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 3 months ago
Applicable Offer Starting Date 1 Mar 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No
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and evaluation of innovative data-driven and Machine Learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop novel optimization
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of Translational Imaging in Oncology at NCT/UCC Dresden is seeking, as of June 2026, a Medical Physicist Reference number: 2026-0030 Our department uses imaging to optimize new therapies and investigates new radio
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Performing simulations on high-performance computing environment Performing parameter studies to identify optimal operating conditions and to support the design and improvement of experimental setups
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AI-based material modeling, focusing in particular on one or more of the following fields: AI-assisted modeling, optimization, and problem inversion in the area of material properties and processes
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entropy or Kullback-Leibler divergence vs. measurement time Analysis and documentation of the results Your Profile: Excellent academic performance in a master’s program in electrical engineering, materials
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Research Framework Programme? Not funded by a EU programme Reference Number JP/001/26 Is the Job related to staff position within a Research Infrastructure? No Offer Description The Faculty of Life Sciences
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prediction or even ecological life cycle assessment. Optimizing manufacturing processes for turbomachines especially aeroengine components are major focus area of the department. Previous research has shown
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detail: Identification of novel electrolyte components (conductive salts, solvents/co solvents and functional additives) and their optimal compositions for the development of advanced non aqueous aprotic
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autonomous electric vehicles under disruptions. This collaborative project aims to develop a resilient, multimodal transport management strategy that leverages automation and communication to optimize