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About the lab The Laboratory of Causal Systems Immunology combines causal inference, probabilistic AI and large-scale in vivo perturbation experiments to uncover how genes shape immune-cell states
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collaboration with our wet-lab team and collaborators, you identify and integrate validation data In collaboration with the VIB.AI ML unit, you manage a large codebase containing the harness, interpretation
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framework for the joint analysis of large-scale structure (LSS) and gamma-ray data. By combining these complementary probes of the same underlying matter distribution, the project aims to sharpen constraints
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postdoctoral fellow will be hosted by one of the faculty members listed below. Candidates must hold a PhD, or expect to be awarded one by the end of the calendar year, and at the time of appointment must be
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, computational biology, genomics, or a related quantitative field. You will have strong programming skills (Python, R, Linux) and experience analysing large-scale sequencing datasets, ideally RNA-sequencing data
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B cell lymphomas including diffuse large B cell lymphoma and Burkitt lymphoma. They arise as a byproduct of somatic hypermutation, when the enzyme activation-induced deaminase (AID) generates DNA
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outcome sets. You will either need a PhD (or be nearing completion), in computer science, data science, artificial intelligence/machine learning (AI/ML), health data science, bioinformatics or a related
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consortium connects AI model development directly to large-scale microbiome data generation, microbial culture collections, greenhouse experiments and field validation. More information For more information
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used by a PhD student, likely starting October 2027, to initiate development of a deep-learning architecture. The refined batch of synthetic data will be used by the PhD student to finalise the deep
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applicable. Preferred Qualifications PhD in data science, and/or public health or related fields including health services research, health informatics, computer science. Experience in data analysis using