Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- UTB
- Tilburg University
- Technical University of Denmark
- Monash University
- AALTO UNIVERSITY
- ETH Zurich
- Forschungszentrum Jülich
- Nanyang Technological University
- Aalto University
- Lulea University of Technology
- NIST
- AIT Austrian Institute of Technology
- Aalborg University
- CEA
- Georgetown University
- Ghent University
- Harvard University
- Heidelberg University
- King's College London
- Pennsylvania State University
- University of California, Los Angeles
- University of Copenhagen
- University of Helsinki
- University of Leeds
- University of Lund
- University of Sheffield
- University of Twente
- Vrije Universiteit Amsterdam
- Vrije Universiteit Brussel
- 19 more »
- « less
-
Field
-
volume estimation, navigation, decision-making, and control modules within a simulation-based validation framework. Develop methods to estimate the safe operational capacity for drone operations under
-
, generate synthetic DAS datasets, develop deep-learning models for seismic event detection, location, and full moment-tensor estimation, and validate these methods using field observations from earthquakes
-
emissions. Developing new statistical methods for the interpretation of atmospheric greenhouse gas data from satellites Building spatially explicit inventories of fluorinated greenhouse gas emissions
-
infrastructure behind our digital twins. You will: develop and benchmark methods for parameter estimation and uncertainty quantification, from classical optimisation and Bayesian inference to physics
-
clinical epidemiology, and working with methods like random forest and targeted learning, the candidate will contribute to the development of interpretable survival models and doubly robust estimation
-
next generation of behavioural modelling methods with applications in transport, health and the environment? Understanding human decision making increasingly relies on analysing very large datasets
-
downstream regression tasks such as biomass estimation and crop yield prediction. GeoFMs are typically pretrained on large-scale EO datasets using self-supervised learning, enabling them to learn general
-
motivated to develop AI methods and apply them to clinically or epidemiologically meaningful research questions. We are particularly interested in candidates with experience in longitudinal data
-
/multiparametric-methods-for-early-detection-of-prostate-cancer), you will lead the development of an AI agent for treatment selection that combines longitudinal clinical data, imaging, outcome prediction and
-
regions. Job description The research focuses on investigating/developing generic methods and algorithms in the context of multi-modal synthetic aperture radar (SAR) for the estimation of environmental