Sort by
Refine Your Search
-
Category
-
Field
-
. scRNA‑seq, scATAC‑seq). Train, evaluate, and benchmark deep learning models operating on single‑cell, regulatory, or multimodal biological data. Support target and mechanism prioritization by integrating
-
processing, quality control, integration, and analysis of single‑cell and multimodal omics datasets (e.g. scRNA‑seq, scATAC‑seq). Train, evaluate, and benchmark deep learning models operating on single‑cell
-
Description We are seeking a motivated new PhD candidate who wants to join an exciting collaborative research program within the VIB-Center for Inflammation Research between the Guilliams, Saelens
-
Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
-
hybrid models that combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and spatial multi-omics data; AI-based modeling of biomolecule structures
-
architectures for (bio)medical research and hybrid models that combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and spatial multi-omics data; AI-based
-
About us VIB.AI, the VIB Center for AI & Computational Biology, is a research center dedicated to integrating machine learning with deep biological insight to understand complex biological systems
-
demonstrated track record in protein structure modelling methods, with hands‑on experience in protein or biologics design and engineering. Hands‑on experience with common machine learning / deep learning
-
‑on experience with common machine learning / deep learning frameworks (eg. PyTorch or JAX) applied to biological or structural data. Solid Python programming skills, with experience building maintainable and
-
of novel mechanistic insights is gained through the application of novel probabilistic deep-learning models that automatically extract biological and statistical knowledge from your in vivo perturbational