Sort by
Refine Your Search
-
Listed
-
Field
-
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
-
, probabilistic modelling, generative AI) or machine learning Proficient in Python or R programming Strong communication skills in English Desirable but not required Preference will be given to candidates with
-
systems biology Background in AI (deep learning, probabilistic modelling, generative AI) or machine learning Proficient in Python or R programming Strong communication skills in English Desirable but not
-
excellent academic performance, especially in relevant subjects, such as mathematics, statistics, machine learning and bioinformatics. You have strong programming skills in Python (R is a plus) You have
-
-of-the-art molecular biology techniques, multimodal data generation and integration, gene regulatory network reconstruction and wide range of machine learning approaches The host labs will provide financial
-
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