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Research Associate to develop and apply scalable artificial intelligence (AI) / deep learning (DL) methods to advance multi-scale coupled physics simulations in support of the missions and programs of the US
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Post-doctoral Fellow/Senior Research Assistant in the Centre for Information Technology in Education
Psychology, Statistics, or a related field. They should have a deep familiarity with modern test theories, and a strong statistical and computing background. The appointee should also have a high level of
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of inflation and phase transitions in the early universe. We are developing new data analysis methods like the use of deep learning and the use of robust statistics. This work is naturally extended to studying
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hold, or are close to completing, a PhD in robotics, robot learning, or a closely related field. You possess strong expertise in deep learning and robot navigation, with hands-on experience in deploying
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employment decision is made. Doctoral degree in remote sensing, geoinformatics, computer science, or a closely related field. You have experience developing and applying deep learning models for Earth
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Intelligence group and the Deep Data Mining group at the Department of Computing Science, collaborating with researchers in, e.g., data science, machine learning, and responsible AI. More info about the research
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-genome deep-sequencing data collected as part of the Office for National Statistics Covid Infection Survey, with a focus on using household data to enable methods for determining who-infected-whom using
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, deep learning, or statistical modeling. Demonstrated experience working with clinical, digital health, or related biomedical data. Proficiency in Python, R, or other scientific programming languages
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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following areas: Probabilistic or Bayesian Machine Learning Variational Inference, Ensemble, or Diffusion Models Spatio-Temporal or Sequential Modelling Graph Neural Networks Deep Learning and Uncertainty