Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- NTNU - Norwegian University of Science and Technology
- Aalborg University
- CNRS
- NTNU Norwegian University of Science and Technology
- Northeastern University London
- Technical University of Denmark
- ;
- ADELAIDE UNIVERSITY
- Abertay University
- Cranfield University
- Duke University
- Empa
- Faculty of Science, Charles University
- Iquadrat Informatica SL
- Linköping University
- Lulea University of Technology
- Max Planck Institute for Sustainable Materials •
- Monash University
- Nature Careers
- Pennsylvania State University
- Queensland University of Technology
- Sorbonne Université, CNRS, Inserm
- Technical University of Munich
- The University of Iowa
- The University of Manchester
- University College Dublin
- University of Amsterdam (UvA)
- University of Birmingham
- University of Bologna
- University of Cambridge;
- University of Exeter
- University of Galway
- University of Nottingham
- University of Surrey;
- University of Twente
- University of Twente (UT)
- University of Vienna
- Uppsala universitet
- cellumation GmbH
- 29 more »
- « less
-
Field
-
community of staff are united by our purpose to inspire Australia’s future change-makers and create a better tomorrow. Work that matters Advance the frontier of AI by developing multi-agent systems capable
-
://cavecore.eu/ Your Research Project (DC4) You will work at the intersection of machine learning, control theory, and autonomous multi-agent systems to develop hybrid learning-based control strategies
-
application! We are looking for a PhD student in automatic control at the Department of Electrical Engineering (ISY). Your work assignments The research area for the position is complex networks and multi-agent
-
; Distributed multi-agent sensing and cooperative positioning algorithms; Machine learning and data-driven methods for ambient awareness. Working Environment: The PhD will be conducted at the University
-
behaviours of multi-agent systems in response to changing internal states and external environmental conditions. Both traditional model-based approaches and modern learning-based control techniques will be
-
-enabled adaptation. The aim is to develop theoretically grounded yet practically deployable algorithms that allow multi-agent robotics to operate robustly in dynamic, uncertain, and interactive environments
-
intelligent, agentic networks—combining causal inference, conformal prediction, and agent-environment modeling—to ensure trustworthy decision support and debugging of autonomous control systems? The PhD will
-
, abandonment, comments, peer interaction) Formalization of algorithms for orchestrating educational AI agents : Train RL and LLM agents and study multi-objective optimization (mastery, well-being stability) Work
-
, and other activities to promote a strong multi-disciplinary and international network between PhD students, postdocs, researchers and industry Duties The project aims to establish a mechanistic