Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Nature Careers
- Cranfield University
- SciLifeLab
- ;
- Technical University of Denmark
- University of Groningen
- NTNU - Norwegian University of Science and Technology
- DAAD
- University of Luxembourg
- University of Nottingham
- Curtin University
- Chalmers University of Technology
- Susquehanna International Group
- ; University of Warwick
- Ghent University
- Swinburne University of Technology
- Technical University of Munich
- ; Cranfield University
- ; Swansea University
- ; University of Birmingham
- ; University of Nottingham
- ; University of Surrey
- Aalborg University
- Abertay University
- Leibniz
- Linköping University
- Lulea University of Technology
- Monash University
- University of Southern Denmark
- University of Twente
- Vrije Universiteit Brussel
- ; Brunel University London
- ; The University of Manchester
- ; University of Bristol
- ; University of Southampton
- AALTO UNIVERSITY
- Canadian Association for Neuroscience
- Ecole Polytechnique Federale de Lausanne
- Empa
- Erasmus University Rotterdam
- Forschungszentrum Jülich
- Imperial College London
- La Trobe University
- Leiden University
- Murdoch University
- Mälardalen University
- National Institute for Bioprocessing Research and Training (NIBRT)
- Norwegian University of Life Sciences (NMBU)
- Purdue University
- Radboud University
- Umeå University
- University of Adelaide
- University of California Irvine
- University of Göttingen •
- University of Limerick
- University of Newcastle
- University of Oslo
- University of Sheffield
- University of Southern California (USC)
- University of Utah
- Utrecht University
- Wageningen University and Research Center
- Østfold University College
- 53 more »
- « less
-
Field
-
engineering, clinical research, and AI-driven health monitoring. This project will explore large-scale maternal datasets—combining clinical cardiovascular assessments with wearable sensor data—to detect early
-
renewable energy, AI-driven engineering, and industrial research. Cranfield’s expertise in wind energy systems, predictive maintenance, and AI applications provides an ideal environment for cutting-edge
-
: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
-
- aware) curtailment tools and the national funded projects Smartlife and Supersized, leveraging model and data-driven digital twins for smart asset management and lifetime optimization of offshore
-
sensing hardware into a high-throughput sorting line is a plus. Proficient in developing, training, and deploying AI-driven sensor-fusion pipelines, including preprocessing, deep-learning model
-
: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
-
diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
-
diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
-
diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
-
diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular