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Field
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/ . The post offers an exciting opportunity for conducting internationally leading research on the whole spectrum of novel machine learning algorithms and practical medical imaging applications, aiming
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material design process. Some potential key research objectives: AI Model Development: Create machine learning models to predict FGM properties based on compositional gradients and processing conditions
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technologies, in coordination with academic and industrial collaborators. Qualifications Applicants must hold a PhD degree in electrical/electronics engineering, telecommunications or related field. Other
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learning, at the intersection of reinforcement learning, deep learning and computer vision, in order to train effective robotic agents in simulation. You should hold a relevant PhD/DPhil (or near completion
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preferred. Proficiency in computational tools such as MATLAB, Python, R, or machine learning applications in immunology is desired. Candidates should have a PhD in Chemistry, Chemical Engineering
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at the Institute of Genetics and Cancer. Informal enquiries may be directed to Dr Athina Spiliopoulou (A.Spiliopoulou@ed.ac.uk ). Your skills and attributes for success: PhD in machine learning, genetic epidemiology
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the development of hierarchical computational materials discovery schemes combining random structure searching, machine learning, atomistic, and density functional theory (DFT) calculations to accurately and
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- commensurate with experience Type of Position Temporary Position Time Status Full-Time Required Education PhD or terminal degree in a related field. Required Related Experience Experience performing experiments
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bone marrow transplantation. We are looking for a highly motivated candidate with a PhD degree interested in using microbiological methods with the aim of developing novel strategies to improve bone
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the system Development of inverse design frameworks using machine learning Development of full simulation for the chip-scale chirped-pulse amplification Use the full simulation to guide system fabrication