Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
Engineering » Computer engineering Engineering » Maritime engineering Engineering » Mechanical engineering Researcher Profile First Stage Researcher (R1) Application Deadline 16 Aug 2026 - 23:59 (UTC) Country
-
18 Jul 2026 Job Information Organisation/Company KU LEUVEN Research Field Engineering » Civil engineering Researcher Profile Recognised Researcher (R2) Application Deadline 31 Aug 2026 - 23:59 (UTC
-
: an excellent Master degree in Computer Science, Artificial Intelligence or a related discipline a background in both the theory and the implementation of neural networks (for instance, transformers, CNNs) and/or
-
candidate) PhD program in business economics (at most one candidate) of the Faculty of Economics and Business of KU Leuven (for more information, see https://feb.kuleuven.be/research/PhD/PhD ). As a PhD
-
14 Jul 2026 Job Information Organisation/Company KU LEUVEN Research Field Computer science » Modelling tools Computer science » Programming Computer science » Systems design Technology » Computer
-
10 Jul 2026 Job Information Organisation/Company KU LEUVEN Research Field Engineering » Mechanical engineering Researcher Profile First Stage Researcher (R1) Application Deadline 8 Aug 2026 - 23:59
-
Framework Programme? Not funded by a EU programme Reference Number BAP-2026-551 Is the Job related to staff position within a Research Infrastructure? No Offer Description ‘Pluriactive Literary Labor and the
-
1 Jul 2026 Job Information Organisation/Company KU LEUVEN Research Field Engineering » Chemical engineering Engineering » Mechanical engineering Engineering » Process engineering Researcher Profile
-
Technology » Biotechnology Technology » Medical technology Researcher Profile First Stage Researcher (R1) Application Deadline 31 Aug 2026 - 23:59 (UTC) Country Belgium Type of Contract Temporary Job Status
-
behaviour. Your ideal background: EU Master Degree in Data Science, Bioscience Engineering, Computer Science or a closely related field Demonstrated understanding of unsupervised machine learning (clustering