Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- The University of Manchester
- Cranfield University
- University of Nottingham
- CNRS
- Nature Careers
- Technical University of Denmark
- NTNU - Norwegian University of Science and Technology
- University of Warwick
- Aalborg University
- Technical University of Munich
- Monash University
- Newcastle University
- Forschungszentrum Jülich
- Leibniz
- University of Basel
- NTNU Norwegian University of Science and Technology
- University of Southern Denmark
- Utrecht University
- Wageningen University & Research
- Delft University of Technology (TU Delft)
- Eindhoven University of Technology (TU/e)
- Inria, the French national research institute for the digital sciences
- Linköping University
- Swedish University of Agricultural Sciences
- Technical University Of Denmark
- Umeå University
- University of Amsterdam (UvA)
- University of Cambridge
- University of Exeter
- University of Sheffield
- ;
- Imperial College London
- Tallinn University of Technology
- University of Antwerp
- University of Birmingham
- University of Copenhagen
- University of Luxembourg
- University of Newcastle
- University of Surrey
- University of Warwick;
- Uppsala universitet
- Vrije Universiteit Brussel
- Cranfield University;
- DAAD
- Empa
- Faculty of Science, Charles University
- George Mason University
- KU LEUVEN
- LEM3
- Luleå tekniska universitet
- MASARYK UNIVERSITY
- Medical University of Innsbruck
- Nantes Université
- Newcastle University;
- Northeastern University London
- Norwegian University of Life Sciences (NMBU)
- Queensland University of Technology
- SciLifeLab
- Slovak University of Agriculture in Nitra
- Swansea University
- The University of Manchester;
- University of Cambridge;
- University of Exeter;
- University of Leeds
- University of Nottingham;
- University of Oslo
- VIB
- Vrije Universiteit Brussel (VUB)
- Wetsus - European centre of excellence for sustainable water technology
- cnrs
- Auburn University
- BRGM
- CEA
- CUNY School of Medicine
- Chalmers University of Technology
- Consiglio Nazionale delle Ricerche
- Crohn’s & Colitis Australia IBD PhD Scholarship
- DIFFER
- ENVT INRAE
- ETH Zürich
- Ecole Centrale de Lyon
- Erasmus University Rotterdam
- European Magnetism Association EMA
- Faculdade de Medicina da Universidade do Porto
- Fundació per a la Universitat Oberta de Catalunya
- Hasselt University
- Helmholtz-Zentrum Geesthacht
- Helmholtz-Zentrum Umweltforschung
- Helmholtz-Zentrum für Infektionsforschung GmbH
- ICN2
- IMDEA Networks Institute
- INRIA
- Institut Català de Nanociència i Nanotecnologia
- Institut National des Sciences Appliquées de Lyon
- Institut de Recherche pour le Développement (IRD)
- Institute of Mathematics and Informatics
- Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S)
- Iquadrat Informatica SL
- Josep Carreras Leukaemia Research Institute (IJC)
- KNAW
- 90 more »
- « less
-
Field
-
challenge is therefore to develop efficient surrogate models capable of rapidly predicting macroscopic mechanical properties directly from microstructural descriptors while preserving the underlying physical
-
for new wind farms. In this context, accurately predicting the propagation of wind turbine noise in the atmosphere is essential to better understand the underlying physical mechanisms and to conduct
-
differential equation models of bacterial persistence. A particular challenge, both for simulation and for machine learning, lies in the high dimensionality of these equations, which causes grid-based numerical
-
and time-series modelling techniques will be used to predict delay and performance degradation. The predictive models will be integrated into a real-time robotic system and evaluated in realistic
-
signatures in communication and coordination data predict collective outcomes — such as group spacing changes, behavioural synchrony, and leadership transitions — beyond what pairwise models capture
-
Bayesian prediction models with uncertainty quantification for trustworthy personalized treatment decisions in the T-PRESS Evidence Ecosystem Framework”. The primary objective of the T-PRESS consortium is to
-
more efficient by identification of the critical processes and parameters. In this project, we aim to develop mathematical and computational models to predict the spatial distribution of concentrations
-
cohort and co-supervised by Prof. Andrew Kao, whose group will provide validated simulation models to benchmark the AI's prediction and Dr. Mikhail Poluektov. As the founding PhD student of the new BASE
-
. The lack of knowledge is related to the models that should be used to auralize UAM in urban environments: new models are needed to predict noise exposure in urban cities. Traditional aircraft noise studies
-
of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field