35 machine-learning-postdoc-"https:" Fellowship positions at UNIVERSITY OF SOUTHAMPTON
Sort by
Refine Your Search
-
would have a background in machine learning, manufacturing, characterization, and testing of novel high performance multifunctional materials. During the project, you will conduct independent research
-
or machine learning. Excellent programming skills in Python and deep learning frameworks A collaborative mindset and interest in socially impactful research. Experience with sign language data, multimodal
-
cell mechanisms, oversee preclinical validation, and advance CAR-iNKT platforms optimised for AML immunotherapy. The role bridges basic and translational research and suits a creative scientist with
-
component disciplines; in explainable multi-modal deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in
-
for light–matter interaction in hyperuniform disordered plasmonic structures, including electromagnetic modelling, optimisation of metal–dielectric–metal resonators, and physics-informed machine-learning
-
Digital Twin Framework for Smart and Sustainable Advanced Manufacturing Research area 3: Advanced Multifunctional Materials The ideal candidates would have a background in machine learning, manufacturing
-
Zamora-Gutierrez (https://www.southampton.ac.uk/people/65cf7s/doctor-veronica-zamora-gutierrez ). The position is fixed for 36 months (3 years). The postholder will lead research on bat ecology and the
-
quantification, or modelling of biological tissues/porous media. Please email Michal Kalkowski m.kalkowski@soton.ac.uk for any informal enquiries. Where to apply Website https://www.timeshighereducation.com
-
(m.kalkowski@soton.ac.uk ) Where to apply Website https://www.timeshighereducation.com/unijobs/listing/409452/research-fellow-in-… Requirements Additional Information Work Location(s) Number of offers
-
application of innovative Machine Learning (ML) frameworks to understand and predict the global hydrological cycle. The role will require bridging the gap between process-based physical modeling and scalable