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driven by curiosity about the physical mechanisms that underlie failure and by the ambition to translate this understanding into more reliable and resilient materials and structures. By combining numerical
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100%, Zurich, fixed-term Prof. Dr. Sereina Riniker's group for Computational Chemistry at the Institute of Molecular Physical Science is interested in the development of methodology for classical
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, Human-Robot Interaction, Imaging Technology, Machine Learning, Medical Informatics, Medical Robotics, Natural Language Processing, Neuroinformatics, Optimization, Physical AI, Probabilistic Models
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qualifications with a university degree (Master or equivalent) in physics, engineering or computer science Proficiency in computer vision and machine learning with Python Knowledge of X-ray physics and laboratory
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in a space with less than 1.2 m of height clearance. The project will take the system from concept through digital modelling and simulation to TRL 3-4 physical prototypes, validated in a representative
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hands-on experience to turn your research into practical impact. Profile You are expected to have completed or will complete a Master’s degree in computer science, machine learning, statistics, physics
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100%, Basel, fixed-term A World-Class Research Environment at the Nexus of Biology, Engineering, and Physical Sciences The Biotechnology and Bioengineering group led by Prof. Dr. Martin Fussenegger
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degree in Physics, Applied Physics, Electrical Engineering, or a closely related discipline A strong interest in experimental physics Excellent analytical and problem-solving skills Enthusiasm
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, oxygen) Use these process-related insights to develop data-driven models to predict water quality under climate change Publish your results in scientific journals and present them at national and
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program (latest by June 2027) You have a background in relevant AI fields or in one of the following: computer science, physics, engineering, applied mathematics Your level of English allows you to discuss