Marc-antoine Jacques
Postdoctoral Fellow at European Bioinformatics Institute | EMBL-EBI
Cambridge, England, United Kingdom
Summary
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Senior🎓
Top SchoolMarc-antoine Jacques is a computational biologist and EMBO‑funded postdoctoral fellow with nine years of interdisciplinary experience at the intersection of biotech, biophysics, and machine learning. He develops and applies deep learning and quantitative methods to decipher single-cell dynamics, cancer biology, microscopy time-series, and cross-species developmental trajectories. Based at Cambridge and EMBL-EBI, he combines rigorous method development with hands-on analysis of large single-cell omics datasets to infer otherwise unobservable biological processes. His PhD work automated analysis of thousands of single-cell signaling trajectories, revealing predictive features tied to phenotypic outcomes—an approach he now scales across species. Colleagues describe him as someone who intentionally mines connections between disciplines to craft creative, reproducible solutions to complex biological questions.
9 years of coding experience
Classe préparatoire BCPST, Biology, Chemistry, Physics, Geology, Mathematics, Classe préparatoire BCPST, Biology, Chemistry, Physics, Geology, Mathematics at Lycée Carnot, Dijon, FranceEngineering degree in biotechnology, Biotechnology, Engineering degree in biotechnology, Biotechnology at École Supérieure de Biotechnologie Strasbourg (ESBS)Doctor of Philosophy - PhD, Computational Biology, Doctor of Philosophy - PhD, Computational Biology at University of BernMaster's Cell Physics, Cell Rheology, Modelling, Systems Biology, Master's Cell Physics, Cell Rheology, Modelling, Systems Biology at UFR Physique StrasbourgMaster's in Bioinformatics and Computational Biology, Bioinformatics, Master's in Bioinformatics and Computational Biology, Bioinformatics at Universität Bern
French, English, German, Chinese
Github Skills (26)
mining9
clustering8
shiny8
codex8
r7
dynamics7
meta-analysis6
time-series6
data-visualization5
deep-learning5
machine-learning5
pytorch5
r-package4
tensorflow4
python3
Programming languages (2)
Github contributions (5)
majpark21/CODEX
Apr 2019 - Oct 2021
Convolutional Neural Network for Dynamics EXploration (CODEX) is a method for mining time-series dataset by leveraging CNN features.
Contributions:147 commits, 18 PRs, 86 pushes in 2 years 6 months
pytorchcodexminingleveragingcnn-features
majpark21/image_analysis
Feb 2018 - Dec 2019
Contributions:52 pushes, 1 branch in 1 year 10 months
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