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Cancer Medicine at the Faculty of Medicine, University of Helsinki. The project will focus on developing approaches for gene regulatory network modeling on deconvoluted bulk data, with applications to pan
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infrastructure data to model how government policy shapes cloud geography. The position can be tailored towards qualitative or quantitative work, depending on the researcher’s background. You will also contribute
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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
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team in the field of cell and developmental biology. Our team uses the developing wing of the Drosophila melanogaster as a model system to investigate how tissue morphogenesis and intercellular
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driving cancer heterogeneity and progression by integrating genomic data into genome-wide regulatory networks. Current projects include (i) modeling distal regulatory interactions, (ii) modeling networks
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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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" button. Applicants are requested to enclose with their applications the following documents as pdf-files: A curriculum vitae List of publications (following the model of the Research Council of Finland
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author) in the field of computational biophysics (molecular dynamics simulations and/or quantum chemistry). Any experience in development and application of AI/ML methods in tandem with modeling and
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to: Develop and apply machine learning models for analyzing complex datasets related to nature conservation. Establish and maintain code and data repositories to ensure efficient workflow and collaboration
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-requisite for the position. Previous experience of working with in vivo models of cancer is considered an advantage. Strong command in written and spoken English is required. The chosen applicant is expected