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to generate baseline datasets for calibrating and validating predictive models of biodiversity-rich forests. Using machine learning (ML) algorithms, the Research Assistant will help predict the occurrence
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following themes: Traffic modelling and multimodal transport simulation Travel behaviour and choice modelling Network optimization and algorithmic methods Machine learning and data-driven approaches
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and museomics Strong bioinformatics skills Proficiency in command-line scripting and programming Knowledge of phylogenomics and population genetics Why conduct a postdoctoral project at the University
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research group. The selected candidate will contribute to two projects: Investigation of genetic predisposition to medications-induced side effects In this project knowledge of FinnGen study large-scale
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recent projects have been published in top level journals such as N.A.R. and Science Advances (Scholar ), focusing on genome-wide studies on bacterial stress responses, new libraries of synthetic genetic
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-throughput imaging or microscopy-linked phenotyping; quantitative image analysis Robust experimental QC and data/metadata handling across many samples and conditions Track C: Evo-devo / functional genetics
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cardiometabolic health: the ability to investigate the interactions of metabolic pathways, such as lipid metabolism, inflammation, insulin resistance, and the gut microbiome. Approaches may include genetics
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mechanisms and intercellular dynamics underlying bidirectional cell production by vascular cambium stem cells. We are utilizing lineage tracing techniques, molecular genetics, proteomics, advanced microscopy
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theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive dynamics, and population genetics. This position is part of the interdisciplinary consortium
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with various methods. By integrating the findings with archaeological knowledge, the goal is to elucidate bacterial genetic evolution that was shaped by human influence and make predictions to the future