Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- NTNU - Norwegian University of Science and Technology
- Tallinn University of Technology
- NTNU Norwegian University of Science and Technology
- CNRS
- Forschungszentrum Jülich
- Chalmers University of Technology
- Cranfield University
- Delft University of Technology (TU Delft)
- Linköping University
- Lulea University of Technology
- Luleå tekniska universitet
- Medical University of Innsbruck
- Swedish University of Agricultural Sciences
- Technical University of Denmark
- University of Antwerp
- University of Birmingham
- Vrije Universiteit Brussel (VUB)
- Wageningen University & Research
- CEA
- Centre de Mise en Forme des Matériaux (CEMEF)
- DAAD
- ENVT INRAE
- Eindhoven University of Technology (TU/e)
- European Magnetism Association EMA
- Helmholtz-Zentrum Dresden-Rossendorf
- Helmholtz-Zentrum Umweltforschung
- IMDEA Networks Institute
- Inria, the French national research institute for the digital sciences
- Iquadrat Informatica SL
- Itä-Suomen yliopisto
- Josep Carreras Leukaemia Research Institute (IJC)
- KU LEUVEN
- Karolinska Institutet, doctoral positions
- Luleå university of technology
- Monash University
- NIOZ Royal Netherlands Institute for Sea Research
- Scuola IMT Alti Studi Lucca
- The Belgian Nuclear Research Centre
- UNIVERSIDAD EUROPEA
- UNIVERSIDAD POLITECNICA DE MADRID
- University College Cork
- University of Amsterdam (UvA)
- University of Bergen
- University of Bristol
- University of Cambridge;
- 35 more »
- « less
-
Field
-
. This in turn, will place a biologically important process into global carbon cycle models and thereby improve predictions of the consequences of ongoing CO2 emissions. YOUR ROLE Within this project, you
-
analysis) to compare brain responses with predictions of computational models (deep neural networks developed by the NASCE team). The objectives include assessing how the brain segments, groups
-
computational models generate hypotheses and, with the help of partner labs, validate them in controlled systems. The end goal is a mechanistic and clinically relevant map of how CIN shapes cancer behavior and
-
of hormonal regulation of gene regulatory networks to predict mechanisms underlying stem cell patterning and plasticity in the shoot stem cell niche. A hybrid modelling approach integrating the dynamics of a
-
response using large public datasets and modern predictive modeling Integrate CIN signatures with functional dependency resources to shortlist candidate vulnerabilities for validation Contribute to open
-
comfort throughout the year in a Nordic climate? Is it possible to predict dynamic outdoor thermal comfort with sufficient accuracy using fast parametric algorithms and machine learning (ML) models instead
-
practices. Read more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about-slu/work-at-slu/ WIFORCE Research School Do you want to contribute to the future sustainable use
-
. We will prioritize hits with suitable predicted drug metabolism and pharmacokinetic properties for optimization using organic synthesis. Finally, you will validate specificity using biophysical methods
-
) to Volumetric Arc Therapy (VMAT), Stereotactic Radiosurgery (SRS), and ultra-high dose-rate (UHDR-FLASH) therapy, the need for real-time control and verification becomes critical. This PhD will further develop
-
-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi