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fellowship is part of the newly established AI Centre for the Empowerment of Human Learning (AI LEARN) . Your immediate leader will be the leader of the Software Engineering unit . About the project This PhD
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The Leibniz-Institut für Analytische Wissenschaften - ISAS - e. V. develops efficient analytical methods for health research. Thus, it contributes to the improvement of the prevention, early
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research center. The role emphasizes building an independent, externally funded research program that leverages AI, genomics, and advanced analytics to improve patient outcomes and reduce health disparities
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multi-data analytics (e.g., health and climate/environmental data) and could include a range of data science methods, such as utilising geographical information systems (GIS), statistical analysis
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sound statistical models and methodology for the data-driven analysis of complex and dynamic systems, with the goal of enabling predictive, prescriptive, and pre‑emptive analytics. We work closely with
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rare diseases. Experience with molecular biology is highly desirable. The ideal candidate demonstrates: A critical and analytical scientific mindset Attention to detail and a structured approach to data
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practice. Key Responsibilities Conduct and support analytical studies involving risk, threat, and security data. Work independently and collaboratively with multidisciplinary teams of varying experience
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Faculty of Engineering and Physical Sciences EPSRC Project Proposals 2026/27 (jobs.ac.uk) Project Link via the 'Apply' button above Project Title: Machine Learning Driven Corrosion Modelling in Bio
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geolocated social media data, and computational techniques from network science and machine learning. It is interdisciplinary, combining theories of healthy and accessible cities with computational data
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data and/or a strong background conducting qualitative research, including experience with in-depth interviews, ethnographic observation and analytic software (such as MAXQDA, NVivoor Atlas.ti