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comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures ( https://CGRlab.github.io/research/ ). About the research project We are open
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Skip to content Bioinformaticians in advanced Machine Learning and AI Stockholm University Share: Facebook Twitter LinkedIn Email Application deadline August 23, 2026 Read more and apply SciLifeLab
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in Machine Learning with a focus on mathematical and statistical methods for uncertainty quantification Uppsala university Share: Facebook Twitter LinkedIn Email Application deadline October 16, 2026
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Information Technology Are you interested in working with machine learning and simulation-based inference for searches for dark matter (or other “invisible” new physics signals) at the Large Hadron
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Information Technology Are you interested in working with probabilistic machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models
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Information Technology Are you interested in developing new image analysis and machine learning methods for precision medicine and clinical decision support? Would you like to work together with
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(National Bioinformatics Infrastructure Sweden: www.nbis.se ) to help shape and accelerate the adoption of advanced machine learning and AI in data-driven Life Science research. At the SciLifeLab
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workday might involve writing and running code to process sequencing data on a computer server, applying statistical models and machine learning algorithms, and performing downstream data analysis
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Python and/or R. Experience in deep learning, machine learning, or large-scale omics data analysis. A high level of self-motivation and independence, good problem-solving skills, and the ability to
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analyses and statistical and machine-learning based modeling, as well as gaining a deeper understanding in extracellular vesicle biology. Work duties and responsibilities The main task for a doctoral