25 condition-monitoring-machine-learning Postdoctoral positions at Durham University in United Kingdom
Sort by
Refine Your Search
-
following: Quantum chemistry (preferably of excited states) Multiscale simulations/environmental modelling Excited state dynamics Data Science/Machine learning in chemistry/Cheminformatics Molecular dynamics
-
matching post based at Teesside University); (3) nature and natural heritage (the focus of a matching post based at Newcastle University); (4) active evaluation for learning (e.g. research conducted by team
-
operation · Application of artificial intelligence or machine learning in energy or engineering systems 5. Strong programming and modelling skills using relevant tools such as Python, MATLAB
-
of turbidity currents in action in the deep-sea, and to develop novel technologies for monitoring these seabed sediment flows. Turbidity currents form the deepest canyons, longest channels and largest sediment
-
in your application. The selection committee will take this into account when evaluating your application. The University has been awarded the Disability Confident Employer status. If you are a
-
Postdoctoral Research Associate position in radar monitoring of natural hazards, beginning around July 2025 (although there is some flexibility in this date, in both directions). The position is funded by NERC
-
in your application. The selection committee will take this into account when evaluating your application. The University has been awarded the Disability Confident Leader status. If you are a candidate
-
scientific computer language (e.g., C, C++), with experience of writing programmes in this language. • Excellent written communication skills in English and demonstrable ability to write material
-
to these changing conditions? The postholder will help to answer these questions using agent-based modelling (ABM) of social-ecological systems, drawing on archaeological and historical datasets collated through
-
state that any computationally hard graph problem satisfying some condition C can still be solved efficiently on a graph class if, and only if, the class has property P. The project is funded by