Dear coastal listers,
Our wave tank facility “Coastal & Ocean Basin – COB” (https://www.cob.ugent.be) at the Flanders Maritime Laboratory on Ostend Science Park (https://ostendsciencepark.be) in Belgium, has an open position for “Principal Engineer of the Coastal & Ocean Basin” (full time position).
Duties include the management of the daily laboratory operations, coordination of the technical team, and planning and execution of the experimental campaigns, in consultation with the COB director.
We offer this unique position via the Ghent University application procedure (full time contract of unlimited duration). Please APPLY ONLY through the UGent e-recruitment system!
Full details are available from https://jobs.ugent.be/job/Oostende-COB-Principal-Engineer-8400/1357806757/. Application deadline is May 27th, 2026.
Feel free to share the vacancy in your network – thanks!
Best Regards,
Peter
|
Peter TROCH Full Professor & Dept. Chair Tel: +32 9 264 54 89 Mail: Peter...@UGent.be Web: awww.ugent.be |
Department of Civil Engineering Coastal Engineering Research Group Technologiepark 60 B-9052 Zwijnaarde Belgium |
Recent journal papers (*UPDATE 2026*):
https://doi.org/10.1016/j.coastaleng.2025.104934:
A novel thin floating plate formulation in SPH: extension to a three dimensional Applied Element Method framework
https://doi.org/10.1016/j.apor.2025.104752:
A 3D experimental methodology for investigating wave-induced pore pressures in the seabed around a monopile foundation
https://doi.org/10.1016/j.apor.2025.104760:
Establishing AEM structural framework within SPH-MBD coupling for hydro-viscoelastic response of very flexible floating structures
https://doi.org/10.3390/jmse13112047:
A computational framework for fully coupled time-domain aero-hydro-servo-elastic analysis of hybrid offshore wind and wave energy systems by deploying generalized modes
https://doi.org/10.1016/j.oceaneng.2025.122077:
Advancing artificial intelligence in ocean and maritime engineering: trends, progress, and future directions
https://doi.org/10.3390/jmse13112083:
Implementing deep learning techniques in port agitation studies under the context of climate change