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, mathematics, applied mathematics, computer science, biomedicine, biotechnology, or another relevant field. Documented experience in programming or quantitative data analysis, for example in Python, MATLAB
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Are you interested in developing new image analysis and machine learning methods for cancer diagnostics and clinical decision support? Would you like to work in a multidisciplinary team together
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perturbation-based GRN inference for single-cell and spatial multi-omics data, to boost GRN quality and add the cell type and tissue heterogeneity dimensions to causal regulatory analysis. A deep learning
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requirements, the applicant must have credits in Life Science, Computer Science Mathematics, Physics or Bioinformatics or alike, including a 30 credit Degree Project (thesis). proficiency in English equivalent
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of approaching reconstruction and variability analysis. The project combines applied mathematics, computational imaging, and structural biology. You will develop algorithms, implement and test software tools, and
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. The applicant is expected to actively perform analysis of spatial transcriptomics and immune clone data, and present results to general and specialized audiences. The data-driven life science initiative Data
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)statistics, (applied) mathematics, computer science, or a related field; candidates from other fields with strong programming/coding skills (see below) are also encouraged to apply. Proficient in at least one
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to demonstrate documented proficiency in English. You have knowledge and expertise in computer vision and/or medical image analysis, deep learning as well as mathematics. You have substantial expertise in
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they are transmitted through populations. Research will have a strong focus on computational analysis or predictive modelling of pathogen biology or host-microbe systems for which multidimensional, genome-scale
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perturbation-based GRN inference for single-cell and spatial multi-omics data, to boost GRN quality and add the cell type and tissue heterogeneity dimensions to causal regulatory analysis. A deep learning