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Field
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imaging with clinical text and decision support. Evaluate algorithms regarding robustness, explainability, and clinical impact in musculoskeletal medicine. Collaborate in an interdisciplinary team
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24.09.2025, Wissenschaftliches Personal The Chair for Efficient Algorithms, led by Prof. Stephen Kobourov, invites applications for a fully funded PhD position at the Technical University of Munich
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reference data, and explore how different stack and channel designs affect shunt current distribution. Ultimately, the results should contribute to optimizing cell and stack configurations for minimal energy
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methods, machine learning algorithms, and prototypical systems controlling complex energy systems like buildings, electricity distribution grids and thermal systems for a sustainable future. These systems
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socially equitable energy supply system based on renewable energies. We contribute to this goal with our research priorities in energy supply, energy distribution, energy storage, and energy use. Through
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Implement and document numerical algorithms for calibration, synchronisation, and data transformation in a modular, traceable workflow Validate Level-1B outputs, including spectral and statistical quality
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Financing yes Type of Position Full PhD Working Language English Required Degree Master Magister (Masters degree course) Areas of study Marketing, Distribution, Commercial Law, Economic Sciences, Economics
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or similar deep learning frameworks GPU know-how: Familiar with GPU workflows and distributed training setups Data competence: Experience in preprocessing, augmentation, and dataset organization; confident
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, based on existing Level-1 documentation provided by the GRACE-FO project Implement and document numerical algorithms for calibration, synchronisation, and data transformation in a modular, traceable
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we