Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Delft University of Technology (TU Delft)
- CNRS
- Inria, the French national research institute for the digital sciences
- SciLifeLab
- University of Oslo
- Uppsala universitet
- Łukasiewicz Research Network - Krakow Institute of Technology
- CEA Paris-Saclay
- Constructor Knowledge Labs gGmbH
- Eindhoven University of Technology (TU/e)
- Forschungszentrum Jülich
- Göteborgs universitet
- Institutionen för Biologi och miljövetenskap
- KU LEUVEN
- NTNU Norwegian University of Science and Technology
- Nantes Université
- Technical University Of Denmark
- The Norwegian School of Sport Sciences
- University of Copenhagen
- University of Twente (UT)
- 10 more »
- « less
-
Field
-
maintenance intervals may be more conservative than necessary. Developing reliable methods to continuously assess the structural condition therefore enable both lighter designs and more efficient maintenance
-
to the development of state-of-the-art nuclear-reaction models and evaluated nuclear-data libraries, supporting safe, reliable, and competitive technologies for both existing and future nuclear-energy systems, as
-
construction (you must not be able to wire authentication around the Auth Resolver); what happens to the language when gear contracts change or a 201st gear is added; projectional representation of graphs with
-
fault detection into better maintenance decisions and more reliable operations. You will work in an applied research environment with railway-sector partners. Your immediate leader will be the Head of
-
science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and
-
-IMPACT ( TURBOmachinery Innovative Manufacturing, Processing, Analysis, Characterization, and Topology; Website: https://cordis.europa.eu/project/id/101311350 ). The TURBO-IMPACT project, funded by
-
activity in vitro; basic knowledge of bioinformatics, molecular modelling and docking and/or structural analysis of protein–ligand complexes, as well as the ability to use in silico results to guide
-
on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
-
to the development of a multi-component framework for reliable and computationally efficient fatigue diagnosis and prognosis of steel structures. Building on the group's established expertise in virtual sensing and
-
, and how strain rate influences material behaviour. The resulting experimental data will be used to validate numerical models and contribute to certification by analysis of future composite structures