- You will develop new AI strategies to learn the genome syntax using large-scale single-cell multi-ome atlases as training data
- You will unravel the diversity of neuronal cell types in the brain of various species (octopus, birds, mammals) through machine-learning & comparative genomics
- You will work very closely together with our wet-lab for data generation and experimental validations
- You will use your AI models to generate synthetic enhancers with new functions, and to functionally interpret non-coding genome variation (health and disease).
- Janssens, Aibar & Taskiran, Nature 2022
- Li & Janssens, Science 2022
- Davie & Janssens, Cell 2018
- Aibar et , Nat Methods 2017
- Bravo et , Nat Methods 2019
- You obtained a PhD degree in Artificial Intelligence, Bioinformatics, Computer Science, Physics, Engineering, Bio-engineering, or
- Proficient in Python programming
- Experience with machine learning is a plus (, Tensorflow/Keras/PyTorch)
- Experience with explainable AI (, SHAP) is a plus
- Experience with high performance computing, software containers
- Experience with single-cell genomics is a plus, but not absolutely essential
- Ability to work independently and in a
- Access to state-of-the-art compute & GPU infrastructure
- A stimulating international research environment
- You can be a driving force in a new EOS consortium across three Belgian Universities
- Competitive salary and benefits
- Fully funded postdoc position for 3 years, but encouraged to apply for an international fellowship (, EMBO, Marie Curie, FWO, HFSP, ).
- Starting date: as soon as possible
- Discover our Center and visit the website or look around during a virtual tour
- Follow us on social media: Facebook and Twitter
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React Full Stack Developer - Dubai, United Arab Emirates - Dice
Description
Description
The Laboratory of Computational Biology (Stein Aerts lab) at VIB-KU Leuven is seeking a postdoc to join our efforts in deciphering the genomic regulatory code underlying cellular identity, with a focus on the
Publications
Recent scientific contributions from the lab include new bioinformatics methods for the analysis of single-cell gene regulatory networks, namely SCENIC and cisTopic; new experimental assays for single-cell ATAC-seq (HyDrop) and for massively parallel enhancer reporter assays; deep learning implementations for enhancer modelling (DeepMEL & DeepFlyBrain); and the generation of multiple single-cell atlases of the fly brain and the fly cell
Check out recent publications:
For all our publications, see
Profile
We offer:
How to apply?
Please complete the online application procedure and include a detailed CV, two reference letters, and a motivation
For more information: