Sort by
Refine Your Search
-
Listed
-
Employer
- Delft University of Technology (TU Delft)
- Eindhoven University of Technology (TU/e)
- University of Amsterdam (UvA)
- Delft University of Technology (TU Delft); yesterday published
- Leiden University
- University of Groningen
- Erasmus University Rotterdam
- University of Amsterdam (UvA); Amsterdam
- University of Amsterdam (UvA); Published today
- Delft University of Technology (TU Delft); 17 Oct ’25 published
- Eindhoven University of Technology (TU/e); Eindhoven
- Leiden University; Leiden
- Maastricht University (UM)
- Delft University of Technology (TU Delft); Delft
- Delft University of Technology (TU Delft); Published yesterday
- Delft University of Technology (TU Delft); today published
- Eindhoven University of Technology (TU/e); 4 Oct ’25 published
- Eindhoven University of Technology (TU/e); Published today
- Eindhoven University of Technology (TU/e); Published yesterday
- Eindhoven University of Technology (TU/e); today published
- Eindhoven University of Technology (TU/e); yesterday published
- Maastricht University (UM); 27 Sep ’25 published
- Radboud University
- University of Groningen; 26 Sep ’25 published
- University of Twente
- University of Twente (UT)
- Utrecht University
- Utrecht University; Utrecht
- Vrije Universiteit Amsterdam (VU)
- 19 more »
- « less
-
Field
-
Are you passionate about developing intelligent algorithms that can support repair and remanufacturing decisions for sustainable manufacturing? As a PhD researcher, you will create innovative
-
in basic control-flow analysis. Process mining, as it stands today, is primarily based on computational techniques and algorithms to analyze and optimize processes. Methods such as process discovery
-
or incomplete. Information Your tasks will include: Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning
-
of hyperbolic deep learning and one PhD student with a keen interest in the algorithmic side of hyperbolic deep learning. Tasks and responsibilities: Conduct high-impact research on hyperbolic deep learning
-
, enabling energy-efficient, quiet, and long-duration monitoring of ecosystems. The research will integrate novel lightweight perception modalities for robust perching in the wild, agile control algorithms
-
tagging algorithm development as well as physics data analysis, with a focus on Higgs boson physics, top quark physics, and searches for new physics signatures. This is what you will do After the discovery
-
, the scattering geometry can be reconstructed mathematically (this is called inverse scattering). This requires both sophisticated mathematical models and efficiently implemented algorithms. In the case of wafer
-
the entire system, where many interconnected modules affect each other. In this project, you will be designing algorithms to guarantee the reliable operation of semiconductor machines, together with a highly
-
systems strong analytical and problem-solving skills fluency in English, both written and spoken Not required, but helpful: Experience with biomedical data/algorithms An affinity for applications
-
Not required, but helpful: Experience with biomedical data/algorithms An affinity for applications of technology Contributions to open source projects This is what we offer A temporary contract for 38