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Field
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Engineering or related disciplines, provided you have a strong interest in communication systems and networking. If you have a solid technical background and are excited about future wireless and satellite
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heterogeneity govern transport dynamics and degradation mechanisms during extended operation. A coupled mechanical–transport framework, accelerated through machine-learning surrogate models trained on multiscale
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12.01.2026, Academic staff The Professorship of Machine Learning at the Department of Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13 100%; initial contract 1.5
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should ideally have experience in: Essential Deep learning and machine learning Computer vision Python programming PyTorch or TensorFlow Strong mathematical and analytical skills Desirable Video
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degree in computer science, mathematics, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph
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, including a mixture of classical and quantum mechanics simulations, cheminformatics and machine learning, as well as collaborative software development, providing expertise for a broad range of future careers
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simulation results with experimental data. This project will integrate advanced AI techniques, including machine learning for parameter optimisation (e.g., Bayesian optimisation, reinforcement learning), AI
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health. Successful candidates may have experience in electron or X-ray microscopy, image analysis, AI and machine learning, quantitative data science or computational modelling. They will be able to work
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-trivial software. Solid understanding of algorithms, numerical methods, scientific computing, or machine learning. Ability and motivation to write clean, maintainable and well-tested code. Strong interest
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adaptive resource allocation, AI-driven network orchestration, dynamic beam steering, joint communication and sensing, and cross-layer optimization. Machine learning techniques will be investigated