Sort by
Refine Your Search
-
Category
-
Employer
-
Field
-
with KU Leuven’s guidelines for PhD candidates (https://www.kuleuven.be/personeel/jobsite/en/phd & https://gbiomed.kuleuven.be/english/phd ). •Extensive opportunities for professional development
-
2027 Is the job 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 Develop system
-
for future transmission systems as systematic, scalable and routinely applicable as RMS-based studies are today. This PhD aims to develop the computational foundations for scalable, automated EMT-based
-
.• A specific PhD topic and a developed study design.• PhD training and intensive supervision.• Research infrastructure and training in its use.• Training and guidance in writing scientific papers.• A
-
the rest of the EMLIT research team at least once every three weeks (a 40-minute train ride). Collaborate with the program’s three supervisors and two other junior researchers in the organization
-
We are seeking a motivated and creative PhD student to develop the next generation of AI-driven protein design methods that explicitly account for protein–lipid and protein–membrane interactions
-
‑situ digital working instructions (DWIs). However, current AR/VR solutions are typically developed as standalone applications, manually authored, and weakly connected to product‑level data. As a result
-
Leuven campus at least three days per week, and travel to Antwerp for meetings with the rest of the EMLIT research team at least once every three weeks (a 40-minute train ride). Collaborate with
-
conferences and workshops. Work on the Antwerp campus at least three days per week, and travel to Leuven for meetings with the rest of the EMLIT research team at least once every three weeks (a 40-minute train
-
of transmembrane β-barrel nanopores. The group provides an interdisciplinary environment where computational method development is closely integrated with experimental validation and large dataset