5 machine-learning scholarships at VIB

PhD student: Machine learning in plant regulatory genomics

RNA-Seq, ChIP/DAP-Seq protein-DNA interaction data, bulk, and single-cell ATAC-Seq) and the application of diverse supervised machine learning approaches (e.g., feature-based, deep learning, and

PhD position in computational neuroscience and machine learning

are currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning. In particular, we develop machine learning methods to derive

PhD student: deep learning in genomics to decode cellular identities in the

. Comprehensive AI background (deep learning, probabilistic modeling, generative AI) Proficient in Python programming Experience with machine learning is a plus (e.g., PyTorch/Tensorflow/Keras) Experience with

PhD student: deep learning in genomics to decode cellular identities in the human brain

modeling, generative AI) Proficient in Python programming Experience with machine learning is a plus (e.g., PyTorch/Tensorflow/Keras) Experience with explainable AI (e.g., SHAP) is a plus Experience with

PhD position in Molecular and Computational Neurogenomics

generation and integration, gene regulatory network reconstruction and wide range of machine learning approaches The host labs will provide financial support for the whole length of the PhD. The applicant will