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Field
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Oxford’s Department of Orthopaedics (NDORMS) as well as collaborators in Bristol and Cardiff. You should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely
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disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with
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Responsibilities: To perform pioneer research in scent digitalization and computation. To further develop machine learning tasks for scent signal classification/fusion. Set up and analyze experiments under different
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together with relevant experience. You will have a strong technical background in machine learning, especially RL and LLMs. An ability to work independently and as part of a collaborative research team is
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, concentration and functional inequalities • Mathematical aspects of machine learning and deep neural networks • Free Probability aspects of Quantum Information Theory. While excellent candidates with other
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, electrical & electronic engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine learning/analysis. Prior research experience
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have a PhD in Civil Engineering, Engineering Mechanics, or Mechanical Engineering. Applicants are expected to demonstrate research experience in the fields of structural modeling and machine-learning
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are demonstrated knowledge related to acoustic modelling, measurement and soundscape. o Essential are demonstrated data analytic skills, ideally with machine learning or statistical modelling • Other general
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Computer Science, Mathematics, Physics, Applied Economics, or a related quantitative field. Skills and Knowledge: Knowledge of scientific computing, data assimilation, and machine learning frameworks. Proficiency in
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web technologies Experience in teaching bioinformatics Previous experience with AI and/or machine learning approaches Interest in reproductive health and/or development of clinical tools and algorithms