Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
industry and academia together to drive pre-competitive, fundamental research in polymers. We welcome applicants with interests in polymer physics, materials processing and characterisation, machine learning
-
different areas of AI (such as machine learning, computer vision, natural language processing, and bioinformatics), hosting powerful computing facilities internally and as part of the EU EuroHPC supercomputer
-
PixHawk Autopilot, Arduino boards, Raspberry Pi - or equivalent Experience with ROS/ROS2 Experience with programming languages like Matlab, Python, C++ Familiarity with machine learning and/or deep learning
-
learning for interconnected systems (e.g., 6G and Edge AI platforms, self-driving vehicle vision) in collaboration with industry partners and domain experts. This PhD thesis is offered in the context
-
, finance or computing science/natural language processing/machine learning. Preference will be given to those who are proficient in Python, adept at processing large-scale data and have worked with large
-
of analytical chemistry and machine learning. For more information please contact Prof. dr. Deirdre Cabooter, mail: deirdre.cabooter@kuleuven.be . Where to apply Website https://www.kuleuven.be/personeel/jobsite
-
Are you interested in developing new image analysis and machine learning methods for cancer diagnostics and clinical decision support? Would you like to work in a multidisciplinary team together
-
. More concretely your work package, for the preparation of a doctorate, contains: Shape the future of immersive visual technologies through optical, computational, and machine‑learning innovation. We
-
12 months by submitting a declaration of non-extension. With appropriate work progress, an extension to a total maximum of 4 years is possible. About the team Join the Responsible Machine Learning (ML
-
declaration of non-extension. With appropriate work progress, an extension to a total maximum of 4 years is possible. About the team Join the Responsible Machine Learning (ML) Group at the Faculty