Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- ; Swansea University
- ; The University of Manchester
- ; Loughborough University
- ; The University of Edinburgh
- ; University of Birmingham
- ; University of Nottingham
- University of Nottingham
- ; Cranfield University
- ; University of Bradford
- ; University of Exeter
- ; University of Oxford
- ; University of Reading
- ; University of Sheffield
- ; University of Warwick
- ; University of York
- Abertay University
- Imperial College London
- University of Cambridge
- 10 more »
- « less
-
Field
-
, particularly in computer networks, operating systems, computer architecture and distributed systems Excellent programming, system building and measurement skills are required Be familiar with, and ideally worked
-
ultraprecise clock distribution (QT Mission 4: positioning, navigation, and timing; QT Mission 5: network synchronisation) and practical quantum sources for hybrid networks (QT Mission 2: quantum communications
-
sweat distribution across impairment groups which may inform future clothing design for improved thermoregulation. Additionally, it will explore cooling interventions, using computational modelling
-
refine simulation tools and machine learning solutions to advance stroke treatment. This involves improving existing computational models that simulate cerebral blood flow, oxygen distribution, and brain
-
interactions/contacts. Monitoring and analysing contact pressure, surface contact distribution, and friction and movement patterns for personalised adjustments to equipment (and training). Based in both
-
duties involve supporting computational infrastructure, coordinating with wider spectroscopic project teams and external science users, contributing to documentation and user manuals, and collaborating
-
areas. Cranfield is part of the national testbed for 6G, researching in the following areas of interest: Real-time specification of 6G telecommunication and edge computing services using Large Language
-
memorisation capabilities of deep learning models. Such vulnerabilities expose FL systems to various privacy attacks, making the study of privacy in distributed settings increasingly complex and vital
-
-shot/Few-shot Learning and Distributed/Decentralized Federated Learning not only provide approaches to combine intelligence but also ensure computational tractability of exponentially growing and