38 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S"-"U.S" positions at University of Twente in Netherlands
Sort by
Refine Your Search
-
Vacancies Postdoc position on Federated/Continual Learning for Time-Series IoT Data (TRUMAN Project) Key takeaways In this role, you will address the intricate challenge of enabling AI to learn
-
Exploiting the Geometric Landscape of Infinite-Dimensional Sparse Optimisation” led by Dr. Marcello Carioni at the University of Twente. More information about this here . Infinite-dimensional modelling has
-
research prototypes that the community can benefit from. The SCS group is internationally recognized in the broad areas of systems, AI, and data security and is unique for its collaborative and friendly
-
as PhD dissertation. Information and application Please apply by 28 February 2026. The application should include: A Curriculum Vitae; A cover letter For more information regarding the topic
-
to what extent data poisoning attacks can influence the output of LLM models in security and safety critical infrastructure. 3. Perform the attack under different scenarios and model the impact. 4. Evaluate
-
, 2020 . In this PhD project you will work on applying RNPU networks for solving computational problems that are considered hard. Information and application Are you interested in this position? Please
-
, fundamental research and/or studies involving matters of scientific urgency. Information and application Are you interested in this position? Please send your application via the 'Apply now' button below before
-
. Designing predictive control strategies that regulate muscle-tendon loading via wearable exoskeletons. Implementing and testing control algorithms in simulation and real-time settings. Information and
-
, exposing the limitations of current detection timelines. This reactive posture is worsened by a visibility gap in the DNS ecosystem. A lack of transparency in registration data, coupled with the short-lived
-
). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will