56 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S" positions at Technical University of Munich in Germany
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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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related discipline. Strong expertise in medical imaging and/or machine learning. Excellent programming and research skills. Interest in translational research and interdisciplinary collaboration with
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skills in Python, Java, C++, etc. A solid foundation in generative AI, machine learning, and related areas. An Interest in eye-tracking technology, Computer Vision, Speech/ Language Processing, VR, and AR
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in a field related to one of the three research areas of MCML: Foundations of Machine Learning; Perception, Vision, and NLP; and Domain-Specific Machine Learning. The Munich Center for Machine
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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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in machine learning and an interest in agentic AI, deep reinforcement learning, and applications in economics. The full-time positions (100%) are initially offered for two years, with the possibility
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our team at the TUM on the ERC project Learning Matters!. Task You will implement learning mechanics in soft matter, specifically in biocompatible hydrogels. Your hydrogels will form the walls of a
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on developing the imaging system as well as novel machine learning approaches for image analysis and disease classification using field data from German and Brazilian agricultural trials. Responsibilities Design
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involve the integration of: Advanced motion planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and
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project Learning Matters!. Task You will break with the current focus on the brain to uncover the physics of continual learning instead by investigating the emergence of learning bottom-up in life, reduced