Bernat Mora is a Principal Biostatistician and data scientist with 11 years of experience applying Bayesian statistics, machine learning, and reproducible pipelines to large-scale environmental and biomedical problems. Trained as a physicist with a PhD in computational biology, he has bridged ecology, immunology, and translational medicine—from modeling climate-driven community shifts at ETH Zürich to analyzing longitudinal SARS-CoV-2 antibody responses for clinical decision-making. He builds containerized, scalable workflows for single-cell and multi-omics integration, has led cross-disciplinary teams, and retained advisory roles with university hospitals. Known for turning complex longitudinal and networked datasets into actionable insights, he seeks opportunities that tackle global environmental and health challenges.
11 years of coding experience
10 years of employment as a software developer
Bachelor's degree, Physics, Bachelor's degree, Physics at Universitat Autònoma de Barcelona
Doctor of Philosophy (PhD), Computational Biology, Doctor of Philosophy (PhD), Computational Biology at University of Canterbury
Master's degree, Biomedical Research, Master's degree, Biomedical Research at Universitat Pompeu Fabra
R package for the randomisation of interaction matrices
Contributions:43 commits, 1 PR, 28 pushes in 2 years 10 months
r-packageinteractionmatricesrandomisation
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