Sort by
Refine Your Search
-
Listed
-
Employer
- Delft University of Technology (TU Delft)
- Utrecht University
- Delft University of Technology (TU Delft); today published
- CWI
- Delft University of Technology (TU Delft); Published today
- Delft University of Technology (TU Delft); Published yesterday
- Delft University of Technology (TU Delft); yesterday published
- Leiden University
- University of Groningen
-
Field
-
to establish a roadmap, (2) developing models and benchmarks for LLM-based refactoring, (3) designing autonomous agents, and (4) conducting studies to analyse real-world impact. We are committed to creating a
-
with unprecedented detail, enabling dynamic, data-driven insight into the recoverable value of materials. Agentic AI systems will be designed to autonomously explore and propose optimal dismantling
-
and hydroeconomics. You have strong quantitative and methodological skills, such as (spatial) data analysis, hydrological modelling, AI-based or agent-based modelling. You have experience with
-
2 Oct 2025 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Process engineering Engineering » Simulation engineering Researcher Profile
-
design principles with stakeholder engagement approaches, ensuring that the developed methods are robust, adaptable, and grounded in real-world practice. You will apply advanced techniques such as agent
-
the vehicle fleet and the multi-objective design of the mixed transporation network. Our key hypothesis is that it is possible to design a mixed network by simulating how to serve a given demand with an
-
hypothesis is that it is possible to design a mixed network by simulating how to serve a given demand with an on-demand ridepooling service, tracking the vehicles’ routes, and allocating fixed lines wherever
-
. Advertising images can also link products to ideas of success. Thus, these forms of communication are not merely tools for conveying messages, but powerful agents that sculpt our society, influence our
-
of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world network data