Sort by
Refine Your Search
-
Category
-
Country
-
Employer
- ;
- Cranfield University
- Chalmers University of Technology
- Technical University of Denmark
- Forschungszentrum Jülich
- ; Newcastle University
- ; Technical University of Denmark
- ; University of Leeds
- Imperial College London
- Leiden University
- Monash University
- University of Twente
- Utrecht University
- ; The University of Manchester
- ; University of Surrey
- Aalborg University
- Aarhus University
- Curtin University
- DAAD
- Linköping University
- Lulea University of Technology
- Nature Careers
- Technical University of Munich
- Umeå University
- Universiteit van Amsterdam
- University of Bern
- University of Copenhagen
- University of Groningen
- University of Newcastle
- University of Southern Denmark
- University of Southern Queensland
- 21 more »
- « less
-
Field
-
older adults. The expected outcome is the creation of AI algorithms to detect early signs of neurodegenerative disorders in older adults living independently at home. The potential benefit is early
-
in Linux/Ubuntu environments is a plus; ● Knowledge of financial modelling or algorithmic trading is beneficial. Bonus points if you have: - Experience with backtesting frameworks or real-time model
-
leverage low-precision accelerators for scientific computing by using a number of tricks, known as "mixed-precision" algorithms. Developing such algorithms is far from trivial. We can look at computational
-
/ . The post offers an exciting opportunity for conducting internationally leading research on the whole spectrum of novel machine learning algorithms and practical medical imaging applications, aiming
-
. Key Accountabilities • Design and develop embedded AI algorithms for appliance profiling using smart meter data • Benchmark performance against state-of-the-art NILM approaches using datasets like
-
thus including sensing systems, tool condition features selection, algorithms for automated signal preprocessing, feature extraction and decision making based on ML and AI. An integral part of
-
achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
-
areas, and be able to creatively combine disciplines to make new research advances in fluid mechanics. You will be creating data-driven algorithms which can solve state estimation problems in fluid
-
formulation, which displays striking similarities to that used by the Computational Fluid Dynamics (CFD) community, has inspired the investigators to adopt conventional CFD algorithms in the novel context
-
novel multi-objective optimisation algorithms, to evaluate metrics such as material circularity, system efficiency, cost, and carbon footprint. The University of Surrey is ranked 12th in the UK in