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will be part of the faculty-wide doctoral education programme. The doctoral programme comprises 25 credits and is offered in two study variants: 25 credits spread over 8 terms (a total of 4 years
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epidemiology and biology of infection with start date 1 September 2026, or as agreed. The future of life science is data-driven. Will you be part of that change? Then join us in this unique program! Data driven
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global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally
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, including the Teacher Education Programmes, the Sport Science Programme, the Human Resource Management Programme, the Behavioural Science Programme with a focus on IT environments, and the Police Education
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at the Faculty of Medicine are enrolled in the faculty-wide doctoral training programme. The programme comprises 25 credits and is offered in two study tracks: 25 credits across 8 semesters (4 years) or across 12
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to develop novel machine learning methods to improve malware detection. The doctoral student position is offered within a research project financed by the Wallenberg AI, Autonomous Systems and Software Program
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description The Department of Political Science is seeking two doctoral students in political science for the project Forest Governance Dilemmas. The call is an initiative within the research program Wallenberg
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, highly motivated for participating in the PhD programme, with strong analytical skills and the ability to work independently. You will be able to shape your project to a significant extent, beginning with
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research program that aims to understand the genetic principles of complex trait plasticity and develop climate-resilient plants using innovative techniques such as omics data interpretation, gene editing
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should have documented background in the following areas: Electrodynamics Data analysis for scientific applications Programming (e.g., Matlab, Python, C, C++) for scientific applications Previous