-
, biomedical engineering, medical imaging, or related field Experience in deep learning with practical implementation Strong Python skills and relevant frameworks Experience with large clinical imaging datasets
-
biologically-inspired deep learning and AI models (NeuroAI). The computational models we work with include vision deep learning models (including topographical, recurrent, or developmentally inspired models
-
expertise in deep learning and representation learning applied to biological data, experience with large-scale multi-omics datasets (such as single-cell and proteomics), and strong programming skills in
-
future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
-
knowledge, research skills and interests in deep learning and large language models to work within established research programmes and contribute ideas and solutions. You will also have the ability
-
attribution, representation analysis, causal probing, and mechanistic circuit analysis. The postholder will also develop predictive models using modern deep learning frameworks (e.g., PyTorch) and evaluate them
-
their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists of three full professors, one associate professor, 6 postdocs and about 15 PhD and 7 master
-
knowledge of deep learning and computer networking/systems is required Experience with AI as a platform and expert use of AI as a tool strongly desired Experience implementing a language model is a plus Refer
-
testing methodologies for physical modelling of deep-water offshore wind turbines. You should hold a relevant PhD (or be near completion) and have a strong publication history. A strong background in
-
Behavior Ability and interest to teach in undergraduate and master's programs in English are essential. Applicants need to have completed their PhD (or be very near completion when applying) as a doctoral