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model with each SNP independently, perhaps adjusting for other covariates such as age and sex. This project will focus on developing and applying novel machine learning and AI methods to improve
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evaluate methods via experiments, benchmarking, simulation and/or real‑world data. The successful candidate will have: A PhD in Statistics, Data Science, Computer Science, Mathematics, or a related field
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Industry Innovation Program Scholarship The Embedded Co-Op Scholarship funded by an Industry Partner via the corresponding Faculty be introduced to allow industry and students to directly interact
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, software, human-computer interaction, ...). We also work very much interdisciplinarily with colleagues from other faculties, e.g. on bio-diversity matters, on physical aspects, on modelling aspects, and on
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to Fisher-Rao geometry and develop theoretical results characterising MML estimation under various regularity conditions. Aim 2: Development of Computational Methods for MML Design and implement efficient
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Species’ distributions are shifting in response to global climate change and other human pressures. Accurate methods to monitor and predict distribution shifts are urgently needed to manage
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Candidates should hold a previous degree (Bachelor’s and/or Master’s) in Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering, with demonstrated knowledge in machine
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relevant field or equivalent experience Strong administrative, numerical and computing skills Experience with systems like SAP and Callista Exceptional organisational and communication skills A proactive
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Lecturer The Opportunity The School of Psychological Sciences is seeking an experienced Senior Lecturer to drive excellence in research methods, research design, statistical analysis and computational
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technologies will affect them. It is our anticipation that the work will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly