Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Cranfield University
- Curtin University
- NTNU - Norwegian University of Science and Technology
- Technical University of Denmark
- GFZ Helmholtz Centre for Geosciences
- Nature Careers
- University of Adelaide
- University of Bergen
- University of Twente (UT)
- Université Laval
- ; Swansea University
- ; Technical University of Denmark
- Arts et Métiers Institute of Technology (ENSAM)
- COFAC
- DAAD
- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); Delft
- Loughborough University;
- Max Planck Institute for Sustainable Materials •
- Norwegian Meteorological Institute
- Polytechnic University of Catalonia
- RWTH Aachen University
- Royal Netherlands Academy of Arts and Sciences (KNAW)
- Technical University Of Denmark
- Technical University of Munich
- Technische Universität Berlin •
- University of Nottingham
- University of Southern Queensland
- University of Twente
- Vrije Universiteit Brussel
- Wroclaw University of Science and Technology
- 21 more »
- « less
-
Field
-
of machine-learning models at catchment to regional scales. LanguagesENGLISHLevelExcellent Additional Information Work Location(s) Number of offers available2Company/InstituteUniversitat Politècnica de
-
, and labor shortages. Currently, most animal monitoring and inspections are performed visually by humans, which is time-consuming and error-prone. Computer vision, leveraging recent advances in deep
-
, « A review of ultrasonic sensing and machine learning methods to monitor industrial processes », Ultrasonics, vol. 124, p. 106776, août 2022, doi: 10.1016/j.ultras.2022.106776. - T. Ageyeva, S. Horváth
-
engineering, electrical engineering, data science, or a related field. Skills in embedded systems development, electronics, or IoT (C/C++, Python, Arduino/ESP32, etc.) OR in machine learning and sensor data
-
Martin Australia invite applications for a project under this program, advancing robotic perception systems through monitoring of their machine learning models. Run-Time Monitoring of Machine Learning
-
railway earthworks. Additionally, the project will integrate environmental data through data fusion and develop automated machine learning tools for anomaly detection and risk assessment. The effectiveness
-
monitoring and health monitoring of the different machine components. To this end, multiple dedicated measurement campaigns have been performed throughout the Belgian offshore zone, resulting in a large in
-
, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
-
analytics (statistical models, machine learning, uncertainty quantification) to monitor and predict cycling travel conditions from various perspectives (safety, crowding, travel time, comfort, etc
-
, Cascais, Riga, Vilnius, Melsungen, Ciampino, Urla and Rhodes. The PhD project will involve: The use of data analytics (statistical models, machine learning, uncertainty quantification) to monitor and