Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- NTNU - Norwegian University of Science and Technology
- CNRS
- Eindhoven University of Technology (TU/e)
- Delft University of Technology (TU Delft)
- NTNU Norwegian University of Science and Technology
- Linköping University
- Technical University of Denmark
- Wageningen University & Research
- Cranfield University
- ETH Zürich
- Forschungszentrum Jülich
- Leiden University
- Newcastle University
- Sveriges Lantbruksuniversitet
- Swedish University of Agricultural Sciences
- University of Bergen
- University of Cambridge
- University of Exeter;
- University of Twente (UT)
- Universität Bern
- Uppsala universitet
- Utrecht University
- VSB - Technical University of Ostrava
- Amsterdam UMC
- Centre for Genomic Regulation
- Computational Imaging Research Lab (CIR), Department of Biomedical Imaging and Image-guided Therapy
- Delft University of Technology (TU Delft); yesterday published
- Dublin City University
- Fraunhofer Institute for Wind Energy Systems IWES
- Fraunhofer-Gesellschaft
- Helmholtz Munich
- Inria, the French national research institute for the digital sciences
- Institute of Physical Chemistry, Polish Academy of Sciences
- Karolinska Institutet, doctoral positions
- Leibniz
- Luleå University of Technology
- Luxembourg Institute of Science and Technology
- Monash University
- Nature Careers
- Rice University
- Tallinn University of Technology
- Technical University Of Denmark
- Technical University of Munich
- The Belgian Nuclear Research Centre
- Universidade de Vigo
- Universitat Autonoma de Barcelona
- University of Berne, Institute of Cell Biology
- University of Birmingham
- University of Exeter
- University of Glasgow
- University of Ljubljana, Faculty of Mechanical Engineering
- University of Sheffield
- 42 more »
- « less
-
Field
-
these challenges by: Developing predictive workload, lead-time estimation, material planning models to capture the high variability in HMLV environments using hybrid AI (combining machine learning, feature-based
-
programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in the prediction and modelling of extreme flood events? Do you want to understand how
-
) 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
-
anticipating crises. Current landslide prediction models, based mainly on rainfall thresholds, become ineffective in the presence of snow cover. Snow acts as a temporary reservoir, storing precipitation before
-
types will change under different climate change scenarios based climate projections. This framework will be ultimately included in a flood prediction model, which will be developed within the VIDI
-
Predictive Model” project financed from the funds of Priority 2 of the European Funds for a Modern Economy Program 2021–2027 (FENG) Action 2.2 First Team, with the Intermediate Institution being the Foundation
-
on stability. Testing the model in standard stirred tank apparatus Refining the model to allow predictability between different types of apparatus. Defining an algorithm for testing enzyme stability
-
to turn partial MRI measurements into meaningful input for predicting optimal sensor phase configurations and feedback control; Identifying pathways towards the integration of domain knowledge about MRI
-
, we aim to create autonomous “self-driving” microscopes that: build statistical models of biological dynamics in real time predict the most informative next experiment execute it automatically on living
-
to predict pKa values of payloads using tabulated steric and electronic descriptors. Synthesize novel PABA-derived linkers and prepare conjugates using model compounds. Measure pKa and release behaviour