Sort by
Refine Your Search
-
their suitability for real-time emergency decision-making. This project addresses this challenge by combining physics-based modelling with data driven surrogate approaches. The first stage of the project will involve
-
)' as the programme of study You will then need to provide the following information in the ‘Further Details’ section: · a ‘Personal Statement’ (this is a mandatory field) - upload a document or
-
with bioprocess modelling and simulation tools (e.g., Aspen Plus, MATLAB, or Python), TEA, and LCA. Strong data analysis, problem-solving, and teamwork skills. Commitment to sustainability and research
-
programmed in advance. If anything changes, it may fail. This project explores how to build more adaptable systems using vision-language-action (VLA ) models. These combine computer vision (to see), natural
-
bone cells toward regeneration. These will be characterised using AFM and rheology, followed by immune–osteogenic co-culture studies to evaluate mechanotransductive signalling. The resulting data will
-
Dr Nadimi (sadegh.nadimi-shahraki@ncl.ac.uk) for more information. Number Of Awards 1 Start Date 1st October 2026 Award Duration 4 Years Sponsor EPSRC Supervisors Lead Supervisor: Dr Sadegh Nadimi
-
experimental test data. This project will harness new software capabilities to better understand bone mechanics, providing insight into the cause of OI fractures and its management. The successful candidate will
-
:8209F Select ‘PhD Water Infrastructure & Resilience (WIRe)' as the programme of study You will then need to provide the following information in the ‘Further Questions’ section: a ‘Personal Statement
-
characterisation, data analysis, and data interpretation. Number Of Awards 1 Start Date 1st October 2026 Award Duration 4 Years Application Closing Date 18th February 2026 Sponsor EPSRC Supervisors Dr.Anjali
-
systems using vision-language-action (VLA ) models. These combine computer vision (to see), natural language understanding (to interpret instructions), and action generation (to respond), enabling robots