Sort by
Refine Your Search
-
Listed
-
Employer
- Cranfield University
- ;
- ; Newcastle University
- ; Swansea University
- ; The University of Manchester
- University of Cambridge
- University of Nottingham
- ; University of Bristol
- ; University of Cambridge
- ; University of Exeter
- ; University of Leeds
- ; Brunel University London
- ; University of Birmingham
- ; University of East Anglia
- ; University of Nottingham
- ; University of Oxford
- ; University of Sheffield
- ; University of Southampton
- ; University of Surrey
- Harper Adams University
- 10 more »
- « less
-
Field
-
address: Dr Fahad Panolan: f.panolan@leeds.ac.uk Project summary The Algorithms group at the University of Leeds (UK) is offering a fully funded 3.5-year PhD studentship on Parameterized Complexity and
-
integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
-
capabilities needed for truly sustainable operations. Research Question: How can AI-enhanced digital twin technologies with advanced optimisation algorithms transform manufacturing processes to achieve
-
, integrity-aware multi-domain navigation benchmark and associated algorithms, tested in realistic operational environments. The outputs will support standardisation efforts, accelerate cross-domain navigation
-
the ranking. However, STV method becomes considerably more complex with encrypted ballots. Our goal is to develop an algorithm/protocol to count encrypted ballot using the STV method. Our first point of
-
objects, by embedding them into a 2 or 3-dimensional space through a representation learning algorithm, has been widely used for data exploratory analysis. It is particularly popular in areas such as
-
, you will contribute to developing closed-loop algorithms for regulating brain dynamics with clinical applications in epilepsy and psychiatric disorders. Number Of Awards: 1 Start Date: September/October
-
" (Supervisor: Prof Timothy O'Leary) uses principles from systems neuroscience to develop reliable, low-power spiking neural networks and learning algorithms for implementation in a new generation of neuromorphic
-
optical setup construction. They will make use of commercial simulation software to test electromagnetic designs, algorithmic coding to design metamaterials, fabrication techniques to produce
-
behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and