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About us: We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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quantitative data analysis methods such as machine learning and behavioural classification in ants is essential. You will be ideal for this position if you are well-organised, eager to learn new skills and
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researchers, under the supervision of Prof David Wedge. Collectively, this team has expertise in the analysis of multilevel omic and imaging data; data integration and machine learning; risk prediction. This
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exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics
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Electrical Engineering or Fluid Mechanics. We welcome applications from those with research and teaching interests in the broad field of electrical engineering, power electronics, machines and drives. We
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culture or legal practice-oriented and legal skills teaching. These posts are open both to those with a research background and to those with legal practice or teaching and learning experience who will be
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural
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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had
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marginal structural models will be extended with machine learning techniques for counterfactual prediction and to support sensitivity analyses Candidate The studentship is suited to a candidate with a strong
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environments Enjoys helping others learn and grow Is confident using lab or workshop equipment like cement mixers, hydraulic presses, and testing machines Has worked with materials like concrete, soil, or timber