Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- ; The University of Manchester
- ; University of Warwick
- University of Nottingham
- ; University of Bristol
- University of Sheffield
- ; University of Sheffield
- ; Swansea University
- ; The University of Edinburgh
- ; University of Birmingham
- ; University of Exeter
- ; University of Oxford
- ; University of Sussex
- University of Cambridge
- ; Lancaster University
- ; University of Nottingham
- ; University of Reading
- University of Manchester
- University of Newcastle
- ; Aston University
- ; Cranfield University
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Loughborough University
- ; Manchester Metropolitan University
- ; Newcastle University
- ; UCL
- ; University of East Anglia
- ; University of Essex
- ; University of Southampton
- ; University of Surrey
- Imperial College London
- UNIVERSITY OF VIENNA
- University of Warwick
- 24 more »
- « less
-
Field
-
Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience Engineering Research Group) Aim: Develop a mathematical model for obsolescence modelling for railway signalling and telecoms
-
in a subject relevant to the proposed PhD project (such as mathematics or theoretical physics) is our standard entry, however we place value on prior experience, enthusiasm for research, and the
-
Mathematics (STEM) through our Athena SWAN Bronze award and action plan, we are members of the Women’s Engineering Society (WES) and Working Families, and sponsors of International Women in Engineering Day. We
-
; gender diversity in Science, Technology, Engineering and Mathematics (STEM) through our Athena SWAN Bronze award and action plan, we are members of the Women’s Engineering Society (WES) and Working
-
have a 1st or high 2:1 degree in electrical/mechanical engineering, physics, mathematics, or related disciplines. Skills in numerical tools and programming are desirable. Any experience in engineering
-
and travel Requirements The candidate should have a 1st or high 2:1 degree in electrical/mechanical engineering, physics, mathematics, or related disciplines. Skills in numerical tools and programming
-
background in Computer Science, Mathematics. Students with interests in machine learning, deep learning, AI, uncertainty quantification, probabilistic methods are encouraged to apply. For eligible students
-
PhD project (such as mathematics or theoretical physics) is our standard entry, however we place value on prior experience, enthusiasm for research, and the ability to think and work independently
-
refer to: https://www.cst.cam.ac.uk/admissions/phd ). If the candidate registers as a full-time PhD student at the University and studies for the degree, the candidate may apply to pay staff rate tuition
-
refer to: https://www.cst.cam.ac.uk/admissions/phd ). If the candidate registers as a full-time PhD student at the University and studies for the degree, the candidate may apply to pay staff rate tuition