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the RTG. This PhD project aims to develop a multi-physical simulation framework for various localization sensors (such as e.g., GPS, IMU, SLAM, RTK) that determines the vehicle’s position, explicitly
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
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(Wrocław University of Science and Technology, Poland), with industrial mentor Dr. Stan Van Gisbergen at SCM, Holland, https://www.scm.com/ . DC10 will be co-supervised by Prof. Claudio Amovilli (University
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soft robot-assisted simulations in the areas of brain machine interaction, wearable haptics, and rehabilitation. The successful applicant will have the following technical experience in: PhD degree in
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interdisciplinary research environment at the interface of computational mechanics, mechanical/civil engineering, and scientific machine learning. The PhD candidate will also have opportunities to present their work
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17 Jan 2026 Job Information Organisation/Company KU LEUVEN Research Field Computer science » Programming Physics » Computational physics Engineering » Biomedical engineering Engineering » Systems
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Skills/Qualifications Successful candidates must have a PhD in Physics, Chemistry, Materials Science, Electrical Engineering or closely related disciplines. Prior publication track record in impacted
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independently as well as in collaborative research efforts. Required Qualifications PhD in Cell Biology, Developmental Biology, Biomedical Engineering, or a related field Proficiency in cell culture techniques
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computing solutions to solve inter- and cross-scale simulations of communication flow. We aim to create real-time, personalizable, and predictive technology. As the Artificial Intelligence and Machine
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on qualifications and prior experience, as well as excellent research infrastructure and an international working environment. Qualifications and skills Experimental Fluid Mechanics position: - PhD in engineering