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description The project focuses on studying the evolution of evolvability using computational simulations. Evidence from evolutionary developmental biology suggests that evolvability can change rapidly in
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, computer simulations and various statistical approaches to increase understanding of the ecological and evolutionary processes that underlie speciation and ecological diversification in the context
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relevant fields of biology (e.g., systems, evolutionary, developmental, computational), physics, mathematics or related fields. In the appointment process, special attention will be given to research skills
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application! We are now looking for Computational Research Assistants in Tumour Evolution and Single-cell Genomics. The position Cancer evolves over time and space, with subclones often acquiring new mutations
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your application! We are looking for a PhD student in evolutionary genetics interested in contributing to a better understanding of the mechanisms that shape mutation rates. Your work assignments
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will combine state-of-the-art computer vision, modeling and archived specimens to determine biotic and abiotic factors driving spatial variation in molt phenology. It will use museum genomics to recover
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bioinformatics and evolutionary models The project will involve limited amount of experimental testing of computation models. A willingness to engage in this work is required. Experience in practical work
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at least 1 million DNA barcodes. The project involves collaboration with a computer vision lab at Linköping University, focused on developing AI-assisted techniques for picking out specimens for genome
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development of phylogenetic methods. The EvonetsLab is supported by a Starting Grant from the European Research Council and a DDLS Fellowship from the SciLifeLab and Wallenberg Swedish program for data-driven
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Starting Grant from the European Research Council and a DDLS Fellowship from the SciLifeLab and Wallenberg Swedish program for data-driven life science. The successful candidate will be working within