14 phd-in-computational-mechanics-"KHALIFA-UNIVERSITY" PhD positions at Radboud University
Sort by
Refine Your Search
-
Listed
-
Field
-
through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Babies have so much to learn in such a
-
later skill acquisition. This research is located at the intersection of cognitive neuroscience and developmental psychology. We are looking for a PhD candidate to join the BabyBRAIN lab . In
-
to your application! As a PhD candidate, you will conduct fundamental research in theoretical particle physics, with close connections to experimental observations. Your project will be supervised by Dr
-
5 Sep 2025 Job Information Organisation/Company Radboud University Research Field Chemistry » Physical chemistry Chemistry » Reaction mechanisms and dynamics Physics » Chemical physics Physics
-
funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you an enthusiastic young
-
to your application! As a PhD candidate, you will conduct fundamental research in theoretical particle physics, with close connections to experimental observations. Your project will be supervised by Dr
-
Deadline 30 Nov 2025 - 22:59 (UTC) Type of Contract Permanent Job Status Not Applicable Hours Per Week 38.0 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the
-
team as a PhD candidate! Chemistry is a science of mixtures. Whether you think of complex formulations for drug delivery to the foam on your cappuccino: the properties of small molecules, polymers and
-
you intrigued by the intricate dynamics of molecular collisions and reactions? Want to operate world-unique equipment? Join us as a PhD candidate and contribute to exciting research on cold and
-
you eager to make AI more sustainable? As a PhD Candidate, you will develop innovative methods for predicting and reducing the energy consumption of large-scale AI systems during their design phase