204 machine-learning-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL" Fellowship positions in United Kingdom
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CAR-iNKT platforms optimised for AML immunotherapy. The role bridges basic and translational research and suits a creative scientist with interests in tumour immunology, T cell engineering, and
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machine learning with rigorous statistical methods to address fundamental questions in cosmology and beyond. The research aims are to develop novel inference frameworks for cosmology and apply them
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laboratory scientists, computational biologists, and clinical academic colleagues, running computer simulations and testing predictions in the lab. About you You should be highly motivated, and ambitious
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Medicine focuses on the interface between psychiatry and medicine, psychiatry and occupation, psychiatry and the military, and psychiatry in different settings. The disorders of interest are those
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relations in space Sustainability and management of orbital space An engaged scholarship approach will be taken to the research, with the post holder involved in rigorous academic and industry collaborations
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of the Department. The research activities of the department fall under eight broad headings, though there is much overlap in practice: Information Engineering (Robotics, Computer Vision and Machine Learning
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will be advantageous. Knowledge of machine learning or reinforcement learning techniques will be advantageous. Proficiency in algorithm development using Python will be advantageous
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BUPA Cash Plan 50% off Tuition Fees for staff dependants CU Reward Scheme New Electric Car Scheme Cycle To Work scheme Our Culture As a global Education Group, equality, diversity, and inclusion
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holder must have: Expertise in advanced theoretical and computational electromagnetics, including high frequency asymptotics. Expertise in artificial intelligence and machine learning. Recent research
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Digital Twin Framework for Smart and Sustainable Advanced Manufacturing Research area 3: Advanced Multifunctional Materials The ideal candidates would have a background in machine learning, manufacturing