Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- ;
- Cranfield University
- University of Nottingham
- ; The University of Manchester
- University of Cambridge
- ; University of Bristol
- ; University of Nottingham
- ; Swansea University
- ; University of Warwick
- University of Sheffield
- ; The University of Edinburgh
- ; University of Sheffield
- ; University of Birmingham
- ; University of Oxford
- ; Newcastle University
- ; University of Exeter
- ; University of Sussex
- University of Liverpool
- University of Newcastle
- ; City St George’s, University of London
- ; Lancaster University
- ; University of East Anglia
- ; University of Reading
- ; University of Southampton
- ; University of Surrey
- Harper Adams University
- THE HONG KONG POLYTECHNIC UNIVERSITY
- UNIVERSITY OF VIENNA
- University of Manchester
- University of Warwick
- ; Aston University
- ; Coventry University Group
- ; Cranfield University
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Loughborough University
- ; UCL
- ; University of Cambridge
- ; University of Essex
- ; University of Leeds
- AALTO UNIVERSITY
- Imperial College London
- Utrecht University
- 32 more »
- « less
-
Field
-
your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had
-
Graduates will develop highly transferable skills, preparing them for careers in: Academia (postdoctoral research at universities and research institutes). Industry (energy sector, environmental consultancy
-
of the assembly of these complex microbial communities using ecological theory and mathematical models. The questions we address are: (1) how does the microbial community change during cultivation
-
approaches (e.g. SPG) as well as the use of machine learning, advanced computing, statistical modelling to explore the stochastic response to complex scenarios. This project offers the opportunity to undertake
-
considered to be self-funded students for the purposes of admission. Applicants should have (or expect to obtain by the start date) at least a first class degree in an Physics, Mathematics, Electrical
-
Kevin Wilson, School of Mathematics, Statistics & Physics Dr Holly Fisher, Population Health Sciences Institute Eligibility Criteria You must have, or expect to achieve, at least a 2:1 Honours degree
-
mechanical engineering, physics, applied mathematics or a closely related subject. Interests on: Structural mechanics and dynamics, Stochastic modelling and uncertainty quantification, understanding
-
Honours degree (or equivalent) in an appropriate discipline such as Computer Science or Mathematics. Applicants are expected to have excellent mathematical skills as well as an interest in discrete
-
Communications Dr Jessica Jay, School of Mathematical Sciences Dr Dan Fretwell, School of Mathematical Sciences Qualifications and Experience We are looking for motivated individuals who are eager to tackle real
-
dedicated PhD student with a 1st class or 2:1 degree in the field of Engineering, Mathematics, Physics, Architecture or Computer Science. A MSc degree in a relevant area is desirable though not necessary