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Field
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strong scientific background with relevant expertise in cell and/or molecular biology. Interest in programming, computational biology and statistic towards high-throughput data analysis is considered a
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an outstanding and ambitious postdoctoral researcher in computational biology to pioneer understanding and modeling of tissue architecture using single-cell and spatial transcriptomics data. The focus will be
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small-scale nitration trials under controlled and safe conditions, analyse the resulting NC with the centre's analysis package and quantify the relationship between feedstock and NC properties. The work
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through a model-driven approach, i.e. a combination of simulation- and data-driven methods and tools with data analysis and machine learning as an important part. The work builds on established theories and
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or heterogeneous environmental datasets Familiarity with spatial analysis, GIS, or geospatial data workflows. Experience with machine learning, modelling, or systems analysis approaches Interest in resilience
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industry and includes practical process work, sample handling, analysis and interpretation of results in relation to the project objectives. Detailed description of the work duties, such as: plan, conduct
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LC-MS/MS method for their analysis in different matrices. The method is further developed by including other fermentation-derived metabolites and is used for analysis of thousands of samples in
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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of stress; biotechnology and metabolic engineering; growth and development of plants; the regulation of gene expression; population genetics, genome analysis and the development of breeding systems. Species
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The Department of forest genetics and plant physiology is part of Umeå Plant Science Centre (UPSC, https://www.upsc.se ) which is a centre of excellence for experimental plant research and forest