Sort by
Refine Your Search
-
, mixed-effects modeling, Bayesian methods, deep learning, variational autoencoders, generative AI). Is an experienced programmer in R and/or Python, and used to working with large datasets and reproducible
-
on developing and studying privacy-preserving methods, such as differential privacy, Bayesian privacy, federated learning and synthetic data. The aim is to enable meaningful analyses, such as identifying disease
-
degree in computer science, mathematics, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph
-
, or a capacity upgrade. Your immediate leader will be the Head of Department. About the project This position is hosted at the aiD Center, a premier research hub dedicated to advancing the role