41 big-data-and-machine-learning-phd Fellowship positions at UNIVERSITY OF SOUTHAMPTON
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cognition and emotion processing. We invite applications from individuals with a background in human experimental psychology (participant recruitment, experimental testing, data analysis) and with a PhD in
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record, and leadership. A PhD in a health-related discipline is essential; a PhD in Psychology would be an advantage, and understanding of qualitative approaches will be useful. You will have excellent
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approaches to understand fundamental biological processes, with the goal of using this information to generate engineered antibodies for use as therapeutics in cancer and autoimmune disease. The appointee is
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research teams across Southampton and partner institutions. Data collection will involve travel throughout Southampton. About you Applications will be considered from candidates who are working towards
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: Genomics, precision medicine, bioengineering, and health data science AI and Digital: Machine learning, robotics, digital health, and cybersecurity Defence and Advanced Manufacturing: Secure systems
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experience in qualitative and quantitative data skills in health research. Applications will be considered from candidates who are working towards or nearing completion of a relevant PhD qualification. The
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to the development of intelligent design workflows using CFD and data-driven methods to assess and optimise next-generation vessel designs. You will collaborate closely with project partners, applying state-of-the-art
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, along with an aptitude for developing new experimental techniques that relate to this exciting research program. To be successful with your application, you will need to demonstrate: An awarded PhD in
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. Responsibilities will include: systematic reviewing and evidence synthesis, project management (coordination and reporting), maintaining high quality research procedures, qualitative and quantitative data collection
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We seek a Research Fellow with a proven publication record to develop resistivity models from marine controlled source electromagnetic (CSEM) data to be acquired in February-March 2026 and to