-
, assessing system performance, stability, and scalability for industrial applications. Candidate Requirements Applicants should hold a First-class (or equivalent) degree in Mechanical, Automotive, Powertrain
-
-partnership MIBTP Programme Code for this project: 167D PhD Biosciences FT (MIBTP) How to apply: To apply, please click on the 'Apply' button above, make an account, and submit an application via the university
-
Project Description: This EPSRC-funded PhD project will investigate how next-generation electric and autonomous vehicles can operate as symbiotic agents within the urban ecosystem—intelligently
-
). Funding notes: For details of the MIBTP project and programme, visit https://warwick.ac.uk/fac/cross_fac/mibtp/phd/supervisors/SSmerdon/#molecular_shape-shifting Eligibility and further details
-
(y.chen.22@bham.ac.uk ). Funding: The project is available through MIBTP funding program. For more details, please see: https://warwick.ac.uk/fac/cross_fac/mibtp/phd/supervisors/ychen References: Shropshire
-
mechanics, and analytical and numerical methods to solve partial differential equations. Excellent oral and written communication skills. Prior experience in computational fluid dynamics or active matter will
-
discover and test candidate molecular and cellular mechanisms underlying the switch between structural brain plasticity and degeneration in response to experience, and how this in turn modifies behaviour
-
problem is global in scope, affecting both developed and developing nations, and demands innovative, scalable solutions. This PhD project aims to revolutionize corrosion prediction by integrating physics
-
discover and test candidate molecular and cellular mechanisms underlying the switch between structural brain plasticity and degeneration in response to experience, and how this in turn modifies behaviour
-
identifying biomarkers associated with infection/inflammation. The PhD student will have a Personal Career Development Plan (PDCP) tailored to the student’s needs, detailing the study program, training