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Engineering, Electrical Engineering, or a closely related field Proven research experience and publication track record in robotic manipulation, deformable object handling, or related domains Hands
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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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resources. Scientific Objectives: Develop a system using foundation models to detect, classify, and track both stationary and moving objects from multimodal sensor data. Integrate self-learning mechanisms
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Robotics, Mechanical Engineering, Electrical Engineering, or a closely related field Proven research experience and publication track record in robotic manipulation, deformable object handling, or related
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closely related field Proven research experience and publication track record in robotic manipulation, deformable object handling, or related domains Hands-on experience with robotic hardware (e.g., robot
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of the Horizon Europe research framework program. The consortium maintains the Program Management Unit (PMU), an organizational unit responsible for program planning and program tracking. We are looking for a
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background in control theory, machine learning, and/or computer vision; A proven track record demonstrating strong problem-solving skills and the ability to conduct independent research (e.g., through
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employed by the TUM. Essential qualities and attributes The ideal candidate would have a proven track-record of creating and deploying mixed-method research designs to study human decision-making. Successful
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(negotiable) Contract Duration: 36 months, full-time employment Trial Period: 6 months Project Tasks and Objectives Objectives Industry: Object detection on construction sites to verify elements of a BIM
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We offer a full-time position as research assistant (E13), limited to three years with the objective to pursue a doctoral degree. Part-time employment is generally possible. Payment will be based