Sort by
Refine Your Search
-
Listed
-
Employer
- Eindhoven University of Technology (TU/e)
- Delft University of Technology (TU Delft)
- University of Twente
- Delft University of Technology (TU Delft); yesterday published
- Leiden University
- University of Amsterdam (UvA)
- University of Amsterdam (UvA); Published today
- University of Twente (UT)
- Erasmus University Rotterdam
-
Field
-
18 Dec 2025 Job Information Organisation/Company University of Twente (UT) Research Field Computer science Mathematics » Algorithms Mathematics » Discrete mathematics Mathematics » Mathematical
-
). Information Key Responsibilities: Develop a generalizable and explainable (gray-box) model for adaptive patient monitoring. Utilize a mixed approach combining real and synthetic data for algorithm development
-
1 will focus on developing new graph-theoretic frameworks for analyzing graph learning models, such as Graph Neural Networks or Graph Transformers. PhD position 2 will focus on designing scalable
-
1 will focus on developing new graph-theoretic frameworks for analyzing graph learning models, such as Graph Neural Networks or Graph Transformers. PhD position 2 will focus on designing scalable
-
stakeholders in the Dutch battery ecosystem to develop and demonstrate the next-generation algorithms and models for the future Battery Management System. The PhD student will work on topics related to: Develop
-
: Develop real-time optimization and hybrid AI models for end-to-end multimodal transport planning under uncertainty. Design synchronization, consolidation, and matchmaking algorithms that align prefab
-
of battery modelling and algorithm development, with a strong emphasis on the data-driven modelling and control aspects. You will contribute to shaping the technologies that underpin a more sustainable and
-
motivation, which includes your preference for performing theoretical and/or algorithmic research (max 1 page); a list of publications or prior projects (max 1 page); the names and email addresses of two
-
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
-
experience (max 2 pages); a letter of motivation, which includes your preference for performing theoretical and/or algorithmic research (max 1 page); a list of publications or prior projects (max 1 page