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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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interested in unique interdisciplinary research, integrating molecular simulations, machine learning, statistical physics, multiscale modeling, and uncertainty quantification. The successful applicant will
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applied mathematics, programming experience, and a passion for a practice-oriented project involving real data and applications. Project Outline: The project focuses on the numerical simulation of blood
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for the civil service of the country (TV-L E13). The position is limited to four years and can start in August 2025 or upon agreement. TUM aims to increase the number of women employees, and applications from
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: Interest in data science and data management Currently enrolled in a university study program, preferably in data science, computer science, statistics, or a related field (from semester 3) Very good
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deformable materials in unstructured environments, with applications in industry, healthcare and domestic robotics. Key Research Topics Robotic manipulation of deformable linear (e.g., cables) and surface
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talented individuals passionate about AI, Human-Computer Interaction, Eye-Tracking, and their responsible applications. Ideal candidates will have: • An M.Sc. degree (or equivalent) in Computer Science, Game
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Computer Science or related fields • Strong background in machine learning • Strong programming skills in Python and experience with deep learning frameworks (PyTorch or similar) • Proficient in spoken and written
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Enrollment as a doctoral candidate in TUM IGGSE, with extensive training program Interested? Then we look forward to receiving your application; please send them by e-mail to jobs.ens@ed.tum.de with “PhD
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experiences in working with remote sensing data, climate data and programming skills (R or Python) are desired. You enjoy working in an international team and you are keen on developing a key set of research