Sort by
Refine Your Search
-
Listed
-
Employer
- Delft University of Technology (TU Delft)
- University of Groningen
- Eindhoven University of Technology (TU/e)
- University of Amsterdam (UvA)
- Leiden University
- Tilburg University
- CWI
- Eindhoven University of Technology
- Erasmus University Rotterdam
- Erasmus University Rotterdam (EUR)
- University of Twente
- University of Twente (UT)
- Utrecht University
- 3 more »
- « less
-
Field
-
provably powerful learning models for graphs will require new mathematical machinery. LOGSMS will combine diverse tools from discrete mathematics, learning theory and machine learning, thus facilitating
-
require new mathematical machinery. LOGSMS will combine diverse tools from discrete mathematics, learning theory and machine learning, thus facilitating the design and analysis of such models. PhD position
-
, Electrical Engineering, Computer Science, or a related field. Strong foundation in biomedical signal processing and hands-on experience with clinical data. Knowledge of information theory, sampling theorems
-
game theory, is an advantage, but certainly not a requirement. What we offer you A temporary contract for 38 hours per week for the duration of 4 years (the initial contract will be for a period of 18
-
transformations, transformative social innovation, and social movement theories is especially relevant. Practical experience with community organising and/or social design is welcome. Competencies: You have a good
-
risks, the candidate will develop theory-grounded questionnaires to be administered across three countries using online surveys. During this 4-year-long project, the PhD student will build on the latest
-
. Our work will be informed by sound theories and supported by empirical data. In this project, we will use AI as an opportunity and address the challenges that emerge due to AI. Our goal is to conduct
-
aims to develop and test fundamental theory in applied organisational research. It covers issues from the micro to the macro level of analysis. The programme has two “centres of gravity” that provide
-
. This goal includes directions such as building hyperbolic vision transformers, making it possible to learn from multiple hierarchies, developing theory and implementations to make hyperbolic learning
-
of thousands of software engineers. Across several tracks, we will develop tools that rethink the future of software engineering. Our work will be informed by sound theories and supported by empirical data. In