Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
perturbation-based GRN inference for single-cell and spatial multi-omics data, to boost GRN quality and add the cell type and tissue heterogeneity dimensions to causal regulatory analysis. A deep learning
-
). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
-
information about us, please visit: www.dbb.su.se . Project description The candidate will develop machine learning (ML) strategies, primarily revolving around interpretable ML and generative AI, to study
-
. Experience with machine (deep) learning frameworks (e.g. PyTorch, TensorFlow). Excellent written and verbal English communication skills, required for daily interaction with an international user base
-
data. Much focus is on large scale analysis based on machine learning, deep learning/AI, as well as handling and analyzing large 3D microscopy data. You will work with shorter and longer projects and