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Application deadline: 26 April 2026 Apply now Strengthen the Adapt! project with digital-historical research into the big history of crisis! As a PhD candidate, you will work under the supervision of Prof
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of Human Geography & Spatial Planning at Utrecht University, and work closely together with partners at the other universities involved in REBUILD, including Prof. Jean-David Gerber at the University of Bern
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. You will combine technical work on machine learning with qualitative analysis of how AI systems are interpreted and used in organisational decision-making. Join the Human-Centred Computing group
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, you will conduct your main research in the Netherlands and Switzerland. You will be affiliated with the Department of Human Geography & Spatial Planning at Utrecht University, and work closely together
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? Join an interdisciplinary PhD project that uses large-scale viral genome and epidemiological data to develop predictive theory for rapidly evolving viruses. You will work at the interface of theoretical
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predictive theory for rapidly evolving viruses. You will work at the interface of theoretical physics, stochastic processes, statistical inference, and epidemiology, with SARS-CoV-2 and influenza as key case
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; chromatin profiling techniques (ChIP-Seq, ATAC-Seq, CUT&RUN or CUT&TAG); single-cell sequencing technologies; linux/bash. Additional Information Benefits an exciting and challenging project to investigate
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transformation; chromatin profiling techniques (ChIP-Seq, ATAC-Seq, CUT&RUN or CUT&TAG); single-cell sequencing technologies; linux/bash. Our offer an exciting and challenging project to investigate the mechanisms
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ethical AI. In this project, you will conduct original research within the thematic scope of your work package, by: Developing adaptive learning systems, in the context of sequential decision-making and
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, citizen engagement, and ethical AI. In this project, you will conduct original research within the thematic scope of your work package, by: Developing adaptive learning systems, in the context of sequential