-
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
-
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
-
. 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
-
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
-
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