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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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epidemiology, statistics, bioinformatics, and cancer biology. We have both “dry lab” and “wet lab” components. Currently the group consists of 1 associate senior lecturer, 1 postdoctoral researcher, 1
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, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid academic background with thorough computational and analytical understanding; Proficiency in programming in Python
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theory, statistical inference, and probabilistic modelling for uncertainty quantification in deep learning, particularly large language models. The focus will be on quantifying and evaluating uncertainty
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including epidemiology, statistics, bioinformatics, and cancer biology. We have both “dry lab” and “wet lab” components. Currently the group consists of 2 postdoctoral researchers, 1 statistician (part-time
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Development Design new statistical and machine learning models tailored to this emerging omics modality. Multimodal Data Analysis Work with high-dimensional datasets combining quantitative RNA features
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data with computational modeling Programming skills in Python, R, or another relevant language. Interest in machine learning, statistical modeling, structural bioinformatics, or analysis of large-scale
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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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R and/or Python, with experience in data integration and statistical analysis. Exposure to RNA therapeutics or functional genomics approaches is an advantage. Strong interest in interdisciplinary