11 web-programmer-developer-"IMT" PhD positions at Manchester Metropolitan University
Sort by
Refine Your Search
-
other clinical partners, the new evidence will be rapidly translated to improve local and national prehabilitation programmes that maximise patient engagement and outcomes. Project aims and objectives Aim
-
-for-profits, technology providers, and universities - to identify relational strategies that foster sustainable and circular practices. The successful candidate will develop deep expertise in building B2B
-
accessible AI. Project aims and objectives The aim of this PhD project is to develop principled and efficient methods for multimodal learning under resource constraints. The research seeks to understand how
-
International Learning) running across the institution to develop a novel AI-XR prototype. The successful candidate will have the unique opportunity to drive the development of a novel AI-XR curriculum framework
-
predict the hair line. Design a classification algorithm to quantify the hair loss severity. Develop a mobile phone app as point of care for people with alopecia. Evaluate novel AI assessments using
-
. Enthusiasm for developing creative, qualitative and participatory research skills. Ability to engage confidently with diverse communities. How to apply Interested applicants are encouraged to contact Dr. Kate
-
-Allah and Dr David Sawtell, which has been developed through a Royal Society Research Grant and a proof-of-concept fund to further investigate new methods for improving the energy efficiency of the plasma
-
into the organisation's sustainability strategy. What you’ll do: Scope and categorise additive manufacturing waste in F1 motorsport to inform target waste streams with recycling potential. Develop solutions for AM waste as
-
methods to examine how neurodiverse mothers navigate workplace reintegration and how organisations can better support them. The Centre for Decent Work and Productivity is developing a reputation for
-
of wind tunnel components in elite motorsport. This interdisciplinary project will develop a data-driven approach to understand and predict how process settings, build orientation, machine variability, and