Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
sizes and frequencies by: Measuring rock fractures from UAV data using manual and automated mapping approaches (e.g., machine learning, convolutional neural networks). Monitoring physical weathering
-
the six universities. For more information, check the individual vacancy pages of the universities, or check the project website www.heritour.eu Opens external About the research project This PhD project
-
, involving a large network of academic and industrial partners across the Netherlands. Information and application Are you interested in this position? Please send your application via the 'Apply now' button
-
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
-
, 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
-
to the premise that the disease is more prevalent in southern Africa than the official data suggest. Birnie is thus ensuring that this ‘neglected’ disease is given the attention necessary to combat it effectively
-
approach that combines semantic material data, design-for-circularity, and hub logistics to scale high-quality reuse in regional infrastructure ecosystems (primary focus: Twente; validation: Brabant). Reuse
-
procedure . It describes what you can expect during the application procedure and how we handle your personal data and internal and external candidates. Apply now Application deadline 24 May 2026 We would
-
. Where you will be working You will work within the Physical-Organic Chemistry department as part of the Big Chemistry Robotlab team. At the Robot Lab, a team of chemists, computer scientists and engineers
-
). 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