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for the AiTASHA project. We welcome applications from candidates who: Have research expertise in statistics with a strong mathematical foundation. Have experience in AI, machine learning or deep learning, and
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reproducible infrastructure while collaborating closely with cross-disciplinary teams across genomics, epidemiology, machine learning, and biomedical science. The role will also involve mentoring junior
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, install, commission and maintain compute and data storage resources supporting the research of the Institute, including Machine Learning. • Assume technical and organisational lead responsibility for one
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conditions. The researcher will also work with team members within the consortium in generating necessary data required for developing a machine learning model for storm surge prediction. Key Responsibilities
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on-site lunch provided Workplace Nursery Scheme (salary sacrifice) College gym Car parking (subject to availability/eligibility) Cycle to work scheme The opportunity to work in a beautiful environment The
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fundamental research, we create widely used open-source software including autodE, cgbind/C3, and mlp-train. Our recent advances in Machine Learning Interatomic Potentials (MLIPs) form the foundation of our ERC
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technology industry experience. We are particularly looking for applicants with degrees and/or experience in: audio, acoustics, psychoacoustics, digital signal processing or machine learning. BSL
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of our existing data infrastructure. This can include web applications, APIs, machine learning tools, and image processing pipelines. Utilise good software development practices such as CI/CD, writing
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outputs. Experience in cardiovascular MRI, cardiac diffusion tensor imaging, and machine learning or deep learning methods applied to imaging is highly desirable. You will be a collaborative and motivated
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synthesis, surface-enhanced Raman spectroscopy (SERS), and Raman imaging of cells is highly desirable. Familiarity with high-dimensional data analysis and machine learning methods is advantageous, as