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and population genetics is required. You should be acquainted or capable of learning the modelling associated with genome-wide association studies (GWAS), landscape genomic analyses, and population
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to teaching or supervision duties. Requirements The successful applicant should have a doctoral degree in statistics, mathematics, machine learning, or other relevant field, and experience in developing and
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, calibration, and the development of analysis tools and software. Our key focus areas are the physics of jets, top quarks, and EWSB, including the development of novel machine-learning methods for high-energy
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and adaptive dynamics of tumour microbiome in colorectal cancer. To do so, we will integrate single-cell genomics, transcriptomics and clinical data using unique novel mouse models, spatial technologies
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models in collaboration with our international collaborators. You would also develop advanced image analysis schemes to analyse the experimental data. Your focus would be to investigate the effect
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using unique novel mouse models, spatial technologies and analytical methods. Postdoctoral Researcher in Functional Cancer Microbiome through the NORPOD program NORPOD is a collaborative postdoctoral
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strong background in any of the following: preclinical models, vascular/cancer biology, or cell biology. Applicants should be comfortable working both independently and as part of a team, with strong
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agent-based modeling or another relevant computational approach for the simulation of managed retreat. We look for a candidate in sustainability or environmental social sciences or a related field who
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, developmental biology, genetics, biomedicine, or another appropriate field. Previous experience in confocal imaging and image analysis, scRNA-seq data analysis, or genetically engineered mouse models is
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the Kilpinen group (iPSC models of NDDs, scRNA-seq, CellPainting of in vitro neurons, multimodal data analysis) and the Kim group (single-cell multiomics, gene regulatory network modelling, smfISH, in vivo