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machine learning or theoretical biophysics or computer science Conditions. Postdoc position funded by Centre Inria at Université Côte d’Azur, France. The position is located in Sophia-Antipolis, French
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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to obtain funding for PhD students. In parallel, applications to FRM and/or Pasteur-MD-PhD-PPU program will be also encouraged. Opportunities for Interdisciplinary Training: Depending on the candidate’s
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The candidate should preferably have a PhD in Computer Science or Robotics with a solid background on deep learning and 3D scene understanding. Experience with LiDAR and Computer Vision is a plus. The candidate
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Virtual laboratory to predict the ability of a fluctuating biomass to satisfy a material use-VARIOUS
the Master’s 2 and the Graduate Programme “Materials Science” option “Innovative materials, advanced technologies and modelling”. These lessons are necessary to study the behaviour of biobased products
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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procedure. In this context, the proposed PhD project aims to develop an innovative strategy to evaluate the efficiency and quality of surgical care. This strategy is based on data science, combining
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researcher who meet the following criteria: Qualifications: A nursing degree with clinical experience in nursing A doctorate (PhD) in a related scientific discipline Field experience in nursing education
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🚨 Fully funded Postdoctoral and PhD fellowships available! Please contact us to apply! The immune system is built in successive waves that differ in cellular origin and fate, including two major
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Description This PhD project bridges computational neuroscience and machine learning to study the mechanisms of active forgetting—or unlearning—through the lens of both biological and artificial systems