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. About the role As Postdoctoral Researcher in Big Data for Cardiovascular Population Health, you will play a key role in applying machine learning, health statistics, and large-scale medical and population
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of research investigating cancer risk and prevention. The appointee will work closely with Professor Ruth Travis, Dr Karl Smith-Byrne and other members of the research team, using large-scale epidemiological
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public health. In this role, you will develop and evaluate novel AI and machine learning methods using large-scale multimodal datasets, contributing to epidemiology-informed foundation models, predictive
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, biomonitoring, and toxicity data. Key responsibilities: Lead UCAM's contribution to Task 5.2, including modelling of respiratory deposition of indoor particulate matter and estimation of exposure to aerosol-bound
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protocols, and test hypotheses and analyse scientific data. You will be expected to contribute ideas for new research projects, develop ideas for generating research income, present detailed research
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(or thesis submitted and awaiting examination) in Psychology, Psychiatry, Epidemiology, Statistics, Health Data Science, Public Health, Criminology, or a related discipline. Experience analysing large-scale
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on probability, partial differential equations, large deviations, stochastic analysis, optimisation and machine learning. The successful candidate will contribute to the activities of the Machine Learning & Data
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Institute of Particle Astrophysics and Cosmology (BIPAC), on research aimed at extracting cosmological information from large-scale structure (LSS) and Cosmic Microwave Background (CMB) probes on very large
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visualizing large-scale, complex data. For the vacant position, we particularly search for a colleague experienced in computational text and/or image analysis and processing platform data. Your future tasks
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At the University of Vienna, almost 11,000 people work together on the big questions of the future. Approximately 7,700 of them are academic staff members. These are individuals who, with