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knowledge of handling sequence data from RNA-seq or other NGS-based approaches. Experience with data analysis in Python/R. Experience with experimental work on plants or other eukaryotic organisms in
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ecophysiological tip-ping points from multiple stressor disturbances (climate change and pollution). We will study complex questions of WHY key biological responses to multiple stressors (climatic stress and
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bioinformatics and analysing high-throughput sequencing data Experience with DNA laboratory methods, preferably ancient DNA The candidate must demonstrate an interest in and be comfortable with doing fieldwork in
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, energy system optimization and possibly machine learning to guide energy transitions towards net-zero systems. The research supervisors have prepared multiple potential projects in this area and will work
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on the development of bioinformatic tools and advanced omics- and biomarker analyses. Highly motivated candidates with strong analytical skills will have excellent opportunities to contribute to multiple high-impact
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with numerical modeling, energy system optimization and possibly machine learning to guide energy transitions towards net-zero systems. The research supervisors have prepared multiple potential projects
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are well aligned with the research activities of one or more of these groups. Applicants are expected to describe clearly how their research interests connect to the ongoing research within the section
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join our team in studying this key issue in plasma acceleration. The research topics involve the coupling of multiple plasma-accelerator stages, as well as how to stabilize these machines with newly
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related to models and multiple sources of data describing ecological dynamics. The PhD project will address the following aims: 1) Develop efficient tools for learning about models from data, 2
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. This is a multidisciplinary project involving collaboration across multiple faculties and departments. The associated PhD position will be hosted at the Department of Mathematics, with a focus on data