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hiring date is as early as December 1, 2025, but can be anytime in 2026. The PhD student is expected to develop and apply statistical methodology for causal inference in observational and experimental data
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qualified candidate to develop research and methodological tools to assess and to improve the welfare of farmed fish, contributing to the sustainability of aquaculture practices in the EU. We offer a 3-year
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-centered design—in close collaboration with national authorities and food producers. Responsibilities and qualifications Your overall focus will be to develop and validate AI-powered tools that support
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genetics and archeology. Project description Your task will be to develop new computational methods to study archaic introgression and applying them to the largest dataset of present-day human genomes
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in their production environment. This PhD project will leverage the extensive phenotypic data generated by CowFIT, in combination with other phenotypes from the Danish Cattle Database, to develop both
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data, which often only provides snapshots in time, neglect coastal waters, and overlook certain species. You will work on integrating diverse data sources to overcome these limitations and develop a more
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of results Completing the required PhD school courses and conducting an external research stay Teaching and supervision of MSc students Desired qualifications and skills: Curiosity and high motivation
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administrative, educational, and legal domains. The successful candidate will become part of the Centre for Machine Learning . The centre is part of the Data Science & Statistics section at the Department
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conversion reactions. The second position is focused on modelling stability of electrocatalyst materials. The aim is to develop a framework to predict metastability of catalyst materials. Among the methods
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limited. We are offering a PhD scholarship for a student to develop ambitious new machine learning strategies for generating AI-ready data. You will work at the frontier of active learning and ML-guided