201 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" PhD positions in United Kingdom
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for personalised healthcare, delivered through health promotion conversations that may otherwise not occur due to NHS capacity pressures. At the same time, enhancing experiential learning, reflective practice, and
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to acquire strong transferable skills (e.g. science communication skills developed by presenting results in group meetings and at national/international research conferences). Applicants should have or expect
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collaborative research environment, with strong links to the NHS, local authorities, and community partners. During the placement, the student will engage with rich cohort data, and learn about the design
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essential, as learning and training will be expected during the PhD study. Funding support After a suitable candidate is found, funding is then sought from the University of Nottingham as part of a
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/ masters degree (chemical engineering, chemistry, physics, environmental chemistry / engineering, or similar) Willingness to learn and adapt to new disciplinary areas. Ability to manage and lead independent
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and interpretable deep learning models to upscale species-level mapping to regional satellite products. Organise co-creation workshops with local stakeholders and generate decision-ready indicators
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related to medicines synthesis. Eligibility and Application: We are seeking an enthusiastic and motivated candidate with a strong willingness to learn new disciplines and innovate to achieve project goals
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to undertake collaborative, team-based research, present your findings at conferences and learn how to make impactful contributions to society. Entry requirements Applicants should have a 2:1 at UG Hons
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of real-time adaptive 3D inspection, dynamically adjusting its measurement strategy based on data quality as well as environmental and scene cues. Positioned at the intersection of robotics, computer vision
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to: Develop automated computer aided design (CAD) and meshing pipelines to generate a library of arterial geometries representing common geometric archetypes (e.g. curved vessels, bifurcations, side branches