Research scientist at @GoogleDeepMind passionate about AI, genomics and biology.
Computational biologist interested in deciphering the genomic regulatory code at vib.ai
Genomics, Machine Learning, Statistics, Big Data and Football (Soccer, GGMU)
Computational biologist @HelmholtzMunich, prof @TU_Muenchen & associate PI @sangerinstitute. Dad of 4 and mountain lover. Department news, see @CompHealthMuc
Bioinformatics PostDoc @GHGA Munich & Passionate Linux Sysadmin
This is the official account of the Technical University of Munich – Technische Universität München (TUM).
Posts from the web communications team at TUM Corporate Communications Center
Website: http://tum.de/en
Legal notice: http://tum.de/legal-notice
PhD Student at Theis and Gagneur lab @TU Munich - Interested in ML, gene regulation and epigenetics 🧬. Previously Cambridge University and Heidelberg University. she/her
💻Systems biology and ML, 🧬 regulatory genomics, divulgació
Postdoc at Nedialkova lab @mpibiochem.bsky.social and @gagneurlab.bsky.social | @humboldt-foundation.de Fellow | EMBO Fellow | PhD @CRG.eu | Professor col·laborador @UOCuniversitat.bsky.social
PhD Student in Computational Biology at TU Munich (Gagneur lab) and Helmholtz Munich (Theis lab)
Interested in rare variants and their effect in Population-scale cohorts
ML for regulatory genomics. PhD student @ Gagneurlab
johahi.github.io
Lab of Dr. Annalisa Marsico, PI at Helmholtz Munich. Machine learning methods for RNA biology, genomics, therapeutics, and biomedicine.
Website: https://www.helmholtz-munich.de/en/icb/research-groups/marsico-lab
Github: https://github.com/marsico-lab
PhD student @gagneurlab.bsky.social (TU Munich and Helmholtz Munich).
Interested in rare variant genetics.
https://shubhankarlondhe.github.io/
News from the Gagneurlab@TUM -- To understand the genetic basis of gene regulation and its implication in diseases.
https://www.cs.cit.tum.de/cmm
The monthly virtual seminar series is designed as a platform for interested Kipoi users and developers and will host talks on the applications of deep learning on biological data.
Agenda and registration at https://kipoi.org/seminar/
