Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Utrecht University
- NTNU - Norwegian University of Science and Technology
- Wageningen University and Research Center
- Technical University of Denmark
- ; The University of Manchester
- Cornell University
- Curtin University
- Radboud University
- University of Groningen
- University of Southern Denmark
- University of Twente
- ;
- ; Swansea University
- Chalmers University of Technology
- Cranfield University
- Forschungszentrum Jülich
- Ghent University
- Leibniz
- Max Planck Institute for Biogeochemistry •
- Mälardalen University
- Norwegian Meteorological Institute
- Norwegian University of Life Sciences (NMBU)
- Umeå University
- University of Adelaide
- Østfold University College
- 15 more »
- « less
-
Field
-
determinants of restoration success (e.g., hydrodynamics and water quality) and include advanced features such as remote data transfer making them highly user-friendly. Calibration of these devices will be done
-
metocean and biogeochemistry models accounting for climate change impacts across Europe (from the Nordic Seas to the Black Sea and Mediterranean Sea) and the Pan-Arctic region. The PhD project focuses on new
-
combination with multi-fidelity response models. The multi-fidelity models may include combinations of physics-based response models, Artificial Intelligence (AI) models and probabilistic methods. Your
-
convection while also capturing the large-scale global circulation. Further into the project, there is the possibility to work with moisture or cloud tracking algorithms, regional models, and validation with
-
quality models, especially for coastal water environments You must be highly proficient in the use of programming languages including, but not limited to python, C++, but also database management
-
utilized to mitigate flooding risks through hydrological modelling and stakeholder engagement.Focusing on the Gothenburg region, the project will: Identify roads suitable for climate adaptation in three
-
Title: Predicting and Improving the Quality of Recycled Plastics Using Advanced Metrology and Data Science Research theme: "Materials Characterisation" "Data Science and Machine Learning in
-
tools (e.g., drones, 3D mapping) for high-resolution geological mapping and rock mass quality assessment. Develop and calibrate numerical models using field data and case studies to simulate various
-
communication for global ocean environments: Deep learning models perform robustly on certain environments since they are developed by data in low signal-to-noise ratio. To develop more robust models, we will
-
and human health risks. The work will require interdisciplinary approaches to integrate and analyze a multi-year dataset that includes both in-situ water quality monitoring data, fish sample analysis