20 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" scholarships at Technical University of Munich
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Optimization (DPO) and reinforcement learning from human feedback, building preference datasets together with clinicians - Build and run a Red Team process with physicians, computer scientists, and patient
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on advanced machine learning and emulation approaches. Key responsibilities: The candidates will be expected to work on the following tasks: - Develop machine learning (ML) methodologies appropriate
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tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in the form of graphs to analyze and predict food-effector systems. Key
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data Your Profile The ideal applicant has a strong background in bioinformatics and/or probabilistic machine learning, as well as experience in omics data analysis, and possesses solid English-language
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Top-ranked Master's degree in robotics, computer vision, system control, machine learning, mathematics, or a related field (background in any of the following); Being excited to make a real impact with
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interaction-rich scenarios. Ideal applicants will have a strong M.Sc. in machine learning, control, or safety, and hands-on experience with robotics. Apply now: https://lnkd.in/dNjmv835. Deadline: ASAP. We
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. MATLAB, C/C++, Python. Highly motivated and keen on working in an international and interdisciplinary team. Applicants with strong background in the following fields are preferred: Machine Learning Formal
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Requirements: Applicants should hold an MSc or Diploma in Engineering, Computer Science or a related discipline. Background in Machine Learning and Artificial Intelligence. Strong programming skills (Python
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Bayesian machine learning to improve risk management for bridge portfolios. We offer a funded PhD position in an excellent research environment. The project Our infrastructure is aging, and decisions about
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control software and machine learning expert. How you will support us: ▪ You will take on responsibilities in the field of control and operation of high-coherence superconducting quantum circuits, with a