Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of Warwick
- University of Nottingham
- Newcastle University
- The University of Manchester
- Cranfield University
- University of Exeter
- University of Birmingham
- Imperial College London
- University of Cambridge;
- University of Newcastle
- University of Sheffield
- King's College London
- Newcastle University;
- University of Greenwich
- University of Plymouth
- Northeastern University London
- UNIVERSITY OF VIENNA
- University of Birmingham;
- University of Warwick;
- Abertay University
- Coventry University Group
- Cranfield University;
- Edge Hill University
- Imperial College London;
- King's College London;
- Middlesex University;
- Swansea University
- The University of Edinburgh
- The University of Edinburgh;
- The University of Manchester;
- University of Bristol
- University of Cambridge
- University of East Anglia;
- University of Exeter;
- University of Leeds
- University of Nottingham;
- University of Surrey
- 27 more »
- « less
-
Field
-
on the adsorption mechanisms of these molecules on the metallic surfaces. In this PhD project we will use state-of-art molecular simulation methods [2,3] to clarify the adsorption and desorption mechanisms of various
-
process and equipment optimisation Application of artificial intelligence, surrogate modelling, and optimisation methods to accelerate exploration of RAM design and operating space. By coupling simulation
-
4Impact doctoral cohort. Your supervisor, Dr Qingwei Bai and co-supervised by Prof. Andrew Kao, whose group will provide guidance in both experimental techniques and modelling simulation. You will benefit
-
@ncl.ac.uk Key Accountabilities Develop and implement novel demographic and evolutionary models that incorporate developmental processes Lead the design and analysis of simulation studies to test hypotheses
-
/AI: Apply data-driven methods to construct reduced-order models that bridge analytical theory & high-fidelity simulation data, enabling rapid drag prediction across surface parameter spaces
-
addresses the "calibration problem" in particulate continuum models and particle simulations. Specifically, it focuses on developing robust methodologies for selecting and parameterising contact models, a
-
approach for use with dry fibre forming and RTM manufacturing processes. Assess if a modelling approach can inform process control and optimisation, leading to a reduced part scrap rate. Build simulation
-
theory, Bayesian inference, Monte Carlo simulation, and statistical analysis of subjective data. Data science and machine learning - big data analytics, surrogate modelling, digital twin development, and
-
, and model assumptions, providing confidence bounds on inferred emission rates. Methodologically, the research will combine CFD simulations, surrogate modelling, and inverse techniques to enable
-
the sustainable packaging of granular and complex fluid materials through virtual prototyping. By coupling the open-source software MercuryDPM for discrete particle modelling with oomph-lib for the simulation