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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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of the mechanisms that lead to defect formation in DED-LB, and improve process control measures to either prevent or mitigate the defects. The additionality of external controls over the laser-matter interactions
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block within this process. You will be embedded both within an experimental and computational team, providing a unique atmosphere where there is expertise to develop the deep-learning models while having
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calibration, electronics (e.g. image sensors), … PhD applicants should hold a MSc degree in astrophysics, physics, or engineering, or have obtained an equivalent diploma. Proficiency in English is required
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27 Aug 2025 Job Information Organisation/Company CNRS Department Institut de Recherches sur la Catalyse et l'Environnement de Lyon Research Field Chemistry Physics Researcher Profile First Stage
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interdisciplinary character. The Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering
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block within this process. You will be embedded both within an experimental and computational team, providing a unique atmosphere where there is expertise to develop the deep-learning models while having
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by the automated system until 10pm of the closing date. Any queries regarding the application process may be addressed to jobs@lshtm.ac.uk . Please quote reference EPH-DPH-2025-13. The supporting
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are looking for candidates within the field(s) of chemistry, physics, nanochemistry, material science or similar. Applicants can have a background from solid state chemistry/physics, chemical egeneering
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is a collaborative studentship between the University of Edinburgh and the National Physics Laboratory (https://www.npl.co.uk/ ). The position will be registered and hosted at the University