Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- ; Newcastle University
- ; The University of Manchester
- University of Cambridge
- University of Nottingham
- Cranfield University
- ; University of Sheffield
- ; Swansea University
- ; University of Nottingham
- ; University of Reading
- Newcastle University
- ; Cranfield University
- ; University of Birmingham
- ; University of Exeter
- ; University of Leeds
- ; University of Warwick
- AALTO UNIVERSITY
- University of Liverpool
- University of Newcastle
- ; Manchester Metropolitan University
- ; University of Bristol
- ; University of Cambridge
- ; University of Oxford
- ; University of Surrey
- University of Oxford
- ; Aston University
- ; Imperial College London
- ; Lancaster University
- ; Midlands Graduate School Doctoral Training Partnership
- ; Queen's University Belfast
- ; The University of Edinburgh
- ; University of East Anglia
- ; University of Essex
- ; University of Plymouth
- ; University of Portsmouth
- ; University of Southampton
- Imperial College London
- KINGS COLLEGE LONDON
- King's College London
- THE HONG KONG POLYTECHNIC UNIVERSITY
- UNIVERSITY OF VIENNA
- University of Sheffield
- 32 more »
- « less
-
Field
-
engineering or a relevant area. An MSc degree and/or experience and good knowledge in gas turbine theory, thermodynamics, Machine Learning, and computer programming will be an advantage. Funding Sponsored by
-
the 'Apply' button, above, quoting code MPB50490525. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up
-
coding and CFD is advantageous but not mandatory—an eagerness to learn and innovate is key! Full training will be provided. Why This Matters Efficient storage technologies are essential for a carbon
-
Fellows, 2 Postdoctoral Researchers and 6 Doctoral Researchers. The group focuses studying physical phenomena in atomically precise materials characterized using scanning tunneling microscopy and non
-
(including Computer Science, Physics, Maths, Engineering) Knowledge of modern machine learning techniques and experience with coding in Python is beneficial (but not a strong requirement) Applicants whose
-
of (or aptitude to learn) quantitative data analysis and coding (e.g. R). Or a background in computer or data science who can demonstrate their ecological or natural history knowledge. Candidates should have a
-
/directory/egegpdfib (via the 'Apply' button above) stating course code EGEGR3 with Project: Enabling a natural capital approach to infrastructure transitions with Dr Edoardo Borgomeo. The University actively
-
or second-class honours degree in engineering, physics, mathematics, computer vision, or a related field. Interest or experience in computational modelling or coding—beneficial but not required. A
-
computational elements, and some coding experience in a programming language (e.g., Python, MATLAB, Julia) are essential. The successful candidate will be supervised by: Dr Michael Short (https://www.surrey.ac.uk
-
experience in computational modelling. It will involve the use of open-source computational fluid dynamics codes, with turbulence modelling and porous media approaches. It will also require the development