Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- CNRS
- DAAD
- Ecole Centrale de Lyon
- Eindhoven University of Technology (TU/e)
- Forschungszentrum Jülich
- Fraunhofer-Gesellschaft
- Zürcher Hochschule für Angewandte Wissenschaft ZHAW
- Amsterdam UMC
- Amsterdam UMC; yesterday published
- Chalmers University of Technology
- Chalmers University of Techonology
- Chalmers tekniska högskola
- Cranfield University
- Curtin University
- Danmarks Tekniske Universitet
- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); yesterday published
- Duke University
- Eindhoven University of Technology
- Eindhoven University of Technology (TU/e); Published yesterday
- Eindhoven University of Technology (TU/e); yesterday published
- Elestor BV
- Fundación IMDEA Materiales
- Fureho AB
- Karlsruher Institut für Technologie (KIT)
- Maastricht University (UM)
- Maastricht University (UM); Maastricht
- Max Planck Institute for Sustainable Materials GmbH, Düsseldorf
- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- Nature Careers
- Technical University of Denmark
- Technical University of Denmark (DTU)
- University of Chemistry and Technology Prague
- University of Luxembourg
- University of Stuttgart
- University of Twente
- University of Twente (UT)
- University of Twente (UT); Enschede
- Wageningen University & Research
- 30 more »
- « less
-
Field
-
design and material handling, enabling first-time-right manufacturing. The predictive quality of these tools relies on accurate constitutive models that describe the behavior of the molten material during
-
-analysis pipelines (statistics, mathematical modeling, and ML) for terabyte-scale 3D histology images, from preprocessing to analysis and validation. Handle and visualize large 3D microscopy datasets. Image
-
, DeepFDM, MINO, etc., but also other methods for generative models in function spaces. Develop multiscale (resolution-invariant) AI models for wave kinematics and sea loads on ships, considering also phase
-
. The developed technologies will be validated in half-cells and full working batteries at industrial partners at TRL 6. Our objectives: Multiscale modelling to better understand RFB behavior and identify optimal
-
, AN/sucrose and KNO₃/HTPB. The resulting datasets will be used to calibrate and validate multiscale, multiphysics constitutive models, linking microstructure to ignition sensitivity, detonation dynamics, and
-
validated in half-cells and full working batteries at industrial partners at TRL 6. Our objectives: • Multiscale modelling to better understand RFB behavior and identify optimal hierarchical pore and
-
Models (JSDM) will be used to predict community changes and species presence by the end of the century. Based on data on local and scientific knowledge, the recruited person will have to define four key
-
at industrial partners at TRL 6. Our objectives: Multiscale modelling to better understand RFB behavior and identify optimal hierarchical shaped pore- and electrode-structure to encounter optimum electrolyte as
-
the microscale up. The developed technologies will be validated in half-cells and full working batteries at industrial partners at TRL 6. Our objectives: Multiscale modelling to better understand RFB behavior and
-
: Multiscale modelling to better understand RFB behavior and identify optimal hierarchical shaped pore- and electrode-structure to encounter optimum electrolyte as well as electrical flow. Prototyping