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-Class Environment: Access to a leading research environment specializing in hardware/software for medical wearables, translational endocrinology, and machine learning for medical time-series. Cutting-Edge
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, Chemistry, Physics, Engineering, Mathematics, Computer Science, Data Science, Machine Learning or Artificial Intelligence a minimum 2:1 undergraduate degree (or equivalent) Excellent spoken and written
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PhD Studentship in Aeronautics: How offshore wind farms and clouds interact: Maximising performance with scientific machine learning (AE0078) Start: Between 1 August 2026 and 1 July 2027
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from reactive to proactive. The goal is to increase transparency and trust in the DNS namespace. Key research activities will include applying machine learning and graph-based techniques to uncover
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: Prof. Walter Karlen Location: University of Ulm, Germany Duration: 3 years Start date: August 2026 at latest The PhD position is based at the Institute of Biomedical Engineering at the University of Ulm
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programming skills. Expertise in developing computer vision and machine learning algorithms would be desirable, highly motivated and enthusiastic about advancing AI for societal impact. Qualifications A high
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Learning, Natural Language Processing, Human-Computer Interaction, Digital Health, Endocrinology Secondments (Preliminary Plan): UiB (Norway): 1–2 months — Patient and caregiver interviews, exploration
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Application Deadline: 28 January 2026 Project Supervisors: Prof Vincent Gaffney Dr Andrew Fraser Project Description: The University of Bradford is inviting applications for a PhD studentship in
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. Research Fields: Artificial Intelligence, Multimodal Machine Learning, Natural Language Processing, Human-Computer Interaction, Digital Health, Endocrinology Secondments (Preliminary Plan): UiB (Norway): 1–2
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series analyses, (2) Earth or planetary remote sensing, (3) Data science approaches, including statistical methods, handling of large datasets, pipeline development and/or machine learning (4) Full stack