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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
ensure local relevance. Required knowledge Artificial Intelligence; Machine Learning; Computer Vision; Bioinformatics; Biomedical Engineering; Neuroscience; Genomics; Medical Physics; Data Science
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motivated, collaborative researcher with: Required Master’s degree completed before the contract starts (e.g., astrophysics, applied physics, computational science, or closely related). Relevant
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candidates with a Master's degree in (computational) chemistry, physics, or materials science who are curious about applied machine learning in the natural sciences. Prior machine learning or Python
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, engineering, physics, statistics, or a related field. Experience with image analysis, computer vision, scientific computing, statistics, applied mathematics, machine learning, or related quantitative
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part of a world leading research environment and contribute to the development for the next generation machine learning tools for power systems. One PhD student will focus on physics-informed neural
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, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another field that provides a sufficient degree of background in computer science, artificial intelligence, mathematics and
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single or multiple strongly coupled quantum dots. Who are we looking for? We are looking for candidates within the field of quantum physics or related areas. Applicants can have any background
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Ittersum (mathematics) 2023: Nico Willem Hendrickx (applied physics) 2022: Sara Issaoun (physics) 2021: Didier Nibbering (mathematics) 2019: Chris Smiet (physics) 2018: Adrian Hamers (space science) 2017: Jochem de Bresser (mathematics)
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Electronic and Photonic Materials division in the Department of Physics, Chemistry, and Biology (IFM). The successful applicant will work in collaboration with the Swedish company Epishine AB on a
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molecular chemical physics in general, while operating within the constraints of near term and emerging quantum hardware. In the long term, this can accelerate materials discovery workflows (design of