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, parental leave, appointments of trust in trade union organizations, military service, or similar circumstances, as well as clinical practice or other forms of appointment / assignment relevant
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Science, Electrical/Computer Engineering, or a related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with
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computational physics, computational materials, and machine learning and artificial intelligence, using the DOE’s leadership class computing facilities. This position will utilize methods such as finite elements
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physical environments. This position focuses on research at the intersection of computer graphics, generative AI, and robotics, encompassing topics such as generative modeling, reinforcement learning, multi
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l'institut du thorax, INSERM, CNRS, Nantes Université | Nantes, Pays de la Loire | France | 26 days ago
LevelPhD or equivalent Research FieldMathematicsEducation LevelPhD or equivalent Skills/Qualifications Must hold a Ph.D. degree in Mathematics / Computer science or Machine Learning. • Be able to work within
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/tools or machine learning algorithms. Good computer programming skills in R/Matlab/PerlPython. Knowledge of basic molecular biology, genomics, and epigenetics. Experience in next-generation sequencing
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, computer science, or a related field. Expertise in causal inference and machine learning (in particular reinforcement learning), and strong experience with programming are desired.. Excellent communication and
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Description The Clinical Artificial Intelligence Lab at NYU Abu Dhabi seeks to improve patient care by developing new machine learning methodologies that tackle unique computational problems in
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, applications of machine learning to particle phenomenology, and lattice QCD, both within the Standard Model and beyond. The particle physics phenomenology group members are: J. F. Kamenik (head), B. Bajc, S
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the next generation of PV technologies for beyond 2030. The new postdoctoral research position will use materials modelling techniques (DFT, molecular dynamics, machine learning potentials) to investigate