Sort by
Refine Your Search
-
Listed
-
Employer
- University of Groningen
- CWI
- Leiden University
- University of Twente
- University of Twente (UT)
- Delft University of Technology (TU Delft)
- Eindhoven University of Technology (TU/e); Eindhoven
- Utrecht University
- Wageningen University and Research Center
- Delft University of Technology (TU Delft); Delft
- Eindhoven University of Technology (TU/e)
- Erasmus MC (University Medical Center Rotterdam)
- Erasmus MC (University Medical Center Rotterdam); Rotterdam
- Leiden University; Leiden
- Radboud University
- Royal Netherlands Academy of Arts and Sciences (KNAW)
- University of Amsterdam (UvA); Amsterdam
- 7 more »
- « less
-
Field
-
Computational Fluid Dynamics (CFD) models; data-based models determined from training/calibration data by system/parameter identification and machine learning. The key challenge is striking a balance between, on
-
, Industrial Engineering, or related discipline; Affinity and/or experience with computer programming, statistical learning, and optimization techniques; A good team spirit and feel at home at the intersection
-
theory, and machine learning. They will have access to a fully equipped lab and benefit from collaborations within the ERC team and across TU Delft. There will be opportunities to present at leading
-
(or equivalent) in Computer Science, Artificial Intelligence, Engineering or a closely related field; Solid background in machine learning and/or evolutionary optimisation; strong programming skills (Python/C
-
researchers in soft robotics, control theory, and machine learning. They will have access to a fully equipped lab and benefit from collaborations within the ERC team and across TU Delft. There will be
-
-learning energy trading algorithms that are able to cope with these challenges. By leveraging real-time data, developed algorithms continuously adapt to market dynamics and respond to changing market signals
-
Computer Science, Artificial Intelligence, Engineering or a closely related field; Solid background in machine learning and/or evolutionary optimisation; strong programming skills (Python/C++); Proven interest in
-
geospatial workflows on an abstract level, using purpose-driven concepts and conceptual transformations; develop AI and machine learning based technology to automate the description and modeling of data
-
(or equivalent) in Computer Science, Artificial Intelligence, Engineering or a closely related field; Solid background in machine learning and/or evolutionary optimisation; strong programming skills (Python/C
-
artificial intelligence, computational cognitive science, human-computer interaction, computer science, information systems, or another relevant field. - A keen interest in pursuing interdisciplinary research