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to courses run by our Organisational Development & Professional Learning team. - All FMH staff are entitled to ten days staff development per year (pro rata). Please speak to your Line Manager about how you
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to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short
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We are a world class research-intensive university. We deliver teaching and learning of the highest quality. We play a leading role in economic, social and cultural development of the North East of
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intelligence experts to generate new projections of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet
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, are expected to rapidly acquire the necessary domain knowledge and be interested in building a career in the field. The postholder will work closely with the experimental team in the School of Biological
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Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance of freedom and support. Join us if you’re passionate about groundbreaking international research
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Ambition. Our Behaviours shape our work culture: We listen and learn, support one another, respect everyone, trust each other, and are bold. Located in Newcastle upon Tyne and London, we are an on-campus
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adaptable approach to work and willingness to collaborate. You should thrive on scientific challenges associated with working on human lymph node tissue, be keen to learn, test and develop new tools and
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to work and willingness to collaborate. You should thrive on scientific challenges associated with working on human lymph node tissue, be keen to learn, test and develop new tools and methods. Application
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experience in machine learning and image analysis for ultrasound images and video. The successful applicant will possess specialist experience conducting fieldwork, particularly in low-resource or rural