Sort by
Refine Your Search
-
Category
-
Employer
- University of Groningen
- Utrecht University
- Wageningen University and Research Center
- ; Max Planck Institute for Psycholinguistics
- CWI
- Delft University of Technology (TU Delft)
- Eindhoven University of Technology (TU/e)
- Erasmus MC (University Medical Center Rotterdam)
- KNAW
- Leiden University
- Rijksinstituut voor Volksgezondheid en Milieu (RIVM)
- University Medical Centre Groningen (UMCG)
- University of Twente
- University of Twente (UT)
- Vrije Universiteit Amsterdam (VU)
- 5 more »
- « less
-
Field
-
Job related to staff position within a Research Infrastructure? No Offer Description The PhD will develop models (using AI and agent-based modeling) that identify policies for positive interactions
-
models and mechanisms that enable AI agents in Hybrid Human-AI teams to learn, adapt, and utilise shared team norms through interactions to ensure their actions are aligned and justifiable. This includes
-
May 2025 In human society, communication is an effective mechanism for coordinating the behaviors of humans. In the field of deep multi-agent reinforcement learning (MARL), agents can also improve
-
outcomes. Our research seeks to bridge this knowledge gap by using Agent-Based Modelling (ABM) to simulate and evaluate the impact of various green infrastructure design scenarios in peri-urban areas
-
of the PhD student based at CWI in Amsterdam will study integrated hydrogen-electricity markets. In particular techniques from Artificial Intelligence and multi-agent systems for modelling new types of markets
-
that would give you an advantage) Experience in computational modelling (e.g., agent-based Bayesian models, cognitive learning models, machine learning, robotics). Experience in annotation software such as
-
. These frameworks will: Identify the causes and responsible agents of liveness breaches, and assess any resulting harm. Support recovery strategies using counterfactual reasoning (“what if?”), guiding programs back
-
. The integration of Knowledge Graphs (KGs) with AI agents will link the data and the actions taken by AI agents. Reinforcement Learning from Human Feedback (RLHF) will enable AI to learn and adapt based on real-time
-
coding systems and ontologies, ensuring compliance with FAIR principles. - AI model innovation: select, adapt, and refine large language models (local, cluster, or cloud-based) and frameworks (Ollama
-
) will enable AI to learn and adapt based on real-time user interactions. The dynamic creation and configuration of AI agents will allow users to customise and deploy agents, primarily powered by Large