Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- CNRS
- Delft University of Technology (TU Delft)
- Inria, the French national research institute for the digital sciences
- NTNU Norwegian University of Science and Technology
- Aalborg Universitet
- Aalborg University
- University of Oslo
- University of Primorska
- Uppsala universitet
- Łukasiewicz Research Network - Krakow Institute of Technology
- ARCNL
- Abertay University
- CEA Paris-Saclay
- DIFFER
- Eindhoven University of Technology (TU/e)
- Fondazione Bruno Kessler
- Forschungszentrum Jülich
- Göteborgs universitet
- Helmholtz-Zentrum Berlin für Materialien und Energie
- Heriot Watt University
- Institutionen för Biologi och miljövetenskap
- KU LEUVEN
- Kaunas University of Technology
- Ludwig-Maximilians-Universität München •
- NTNU - Norwegian University of Science and Technology
- Nantes Université
- Newcastle University
- Personalabteilung der Montanuniversität Leoben
- RMIT University
- SciLifeLab
- Technical University Of Denmark
- Technological University Dublin
- The Norwegian School of Sport Sciences
- Tilburg University
- UNIVERSITY OF VIENNA
- University of Birmingham
- University of Copenhagen
- University of Luxembourg
- University of Nottingham
- University of Potsdam •
- University of South-Eastern Norway
- University of Southern Denmark (SDU)
- University of Twente (UT)
- University of Vienna
- 34 more »
- « less
-
Field
-
on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
-
beam (FIB) imaging - can be combined with AI to reconstruct nanoscale chip structures and infer functional behaviour from physical layouts. The project addresses the challenge of extracting reliable
-
sits at the intersection of AI Safety and Data-Centric AI. We aim to make large-scale ML more reliable, transparent, and aligned with human values. We are specifically interested in: Data-centric AI
-
mechanistic and reliable service-life prediction models for concrete infrastructure, supporting improved durability design, maintenance planning and resilience of reinforced concrete structures exposed
-
structures with extraordinary precision and do so quickly enough to keep up with large-scale production. This creates a fascinating computational challenge: how can we infer hidden physical properties from
-
sits at the intersection of AI Safety and Data-Centric AI. We aim to make large-scale ML more reliable, transparent, and aligned with human values. We are specifically interested in: Data-centric AI
-
) are increasingly used in civilian and defense applications such as surveillance, environmental monitoring, infrastructure inspection, etc. However, the platforms are mostly produced with passive structural
-
to the development of a multi-component framework for reliable and computationally efficient fatigue diagnosis and prognosis of steel structures. Building on the group's established expertise in virtual sensing and
-
qualifications: Knowledge of polymer processing and statistical experimental design (DoE) Collaborative mindset and enthusiasm for working in interdisciplinary teams Strong organizational skills and a structured
-
and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured biological data are increasingly common in modern