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candidates should have Solid working knowledge of software tools and environments for application deployment, optimization, and performance analysis Background in modern machine learning models, such as
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for a closed-loop decision support system that can be verified in rehabilitation in many health conditions. This position is open for a postdoctoral researcher in the field of transparent machine learning
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position in the area of Machine Learning for Engineering Design under the guidance of Prof. Mark Fuge, the Chair of Artificial Intelligence in Engineering Design. The general area of the laboratory covers
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-grained activity detection using electromyography sensors as input. Supplemental input modalities will include inertial sensors (IMU). The research will focus on machine learning-based signal processing and
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physical validation. You will regularly present your work at international robotics and machine learning conferences. Your responsibilities will also include supervising bachelor and master students in
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at ETH Zurich. Our mission is to accelerate chemical discovery using digital tools. We predict chemical reactivity and molecular properties using the tools of machine learning, computational chemistry, and
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tendon systems, incorporating cutting-edge sensing and proprioceptive technologies, and controlling the robot through advanced control algorithms and machine-learning techniques. Job description The
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Your position Research position with clinical involvement as part of a Swiss National Science Foundation (SNSF) project “Learning-based 3D Infant Face and Head Model for Medical Applications”. You
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motivated Engineer with a training in machine learning techniques to join our Efficient Particle Accelerators project team. In this role, you'll join the Accelerator Systems Department (SY) , more
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testing of the interactive interfaces. The visual interface will be built with Apple Vision Pro. Your profile Solid programming experience in C#/Unity, Python Knowledge of human-computer interaction