Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
theories from tractable models (probabilistic circuits) and Bayesian statistics to tackle the reliability of machine learning models, touching topics such as uncertainty quantification in large-scale models
-
courses within the research methodology (Data Collection) and statistics modules (Mixed Models, Bayesian Approaches, Network Models, etc.). Additionally, the candidate may also contribute to teaching across
-
such as imaging, spatial, network, and genomics data. The appointment is expected to begin on August 15, 2026. Review of applicants will begin on November 17, 2025, but the position will remain open until
-
with modeling, time-series analysis, or network analysis. Oberlin is situated on the outskirts of Cleveland, combining a cozy small-town atmosphere with the cultural amenities of a major city. The
-
. There will also be common meetings with the other 14 PhD students in the doctoral network, including 3 training schools. As a participant of the project, the PhD student will become part of a team at DTU
-
transportation systems modeling and simulation that could involve integrated machine learning and network equilibrium/simulation, surrogate models/ reduced order emulators or Bayesian or interpretable machine
-
Denmark and Politecnico Di Torino, Italy. There will also be common meetings with the other 14 PhD students in the doctoral network, including 3 training schools. As a participant of the project, the PhD
-
, and engagement with emerging technologies and societal needs in areas such as: Advanced transportation systems modeling and simulation that could involve integrated machine learning and network
-
collaboration networks. For more information about working at the University of Helsinki and living in Finland, please see https://www.helsinki.fi/en/about-us/careers . A diverse and equitable study and work
-
plasma-material interactions in fusion energy systems. You will also advance knowledge of key AI methods such as deep learning, operator learning, and Bayesian optimization, and apply it to develop next