45 associate-professor-computer-"https:"-"https:"-"https:"-"https:" positions at Manchester Metropolitan University
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16 March, to be completed by Wednesday 18 March Stage 2: competency-based interview and stakeholder panel in person on Monday 30 March Stage 3: a conversation with our Director of EDI and Associate
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competing priorities in a fast-moving environment. You will also have: A recognised project or programme management qualification (e.g. PRINCE2, Agile, PMI, MSP or APM). Experience managing complex projects
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, including teaching and on programme assessment, of those they are assessing. ABOUT YOU You will have: • Excellent communication and organisational skills • Strong specialist knowledge in Creative Digital
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in the course of the disease would result in better outcomes for the patient. We are looking for candidates to research computer vision and artificial intelligence techniques to create an objective
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protection legislation. manage student appointment bookings, ensuring all associated tasks are completed and that waiting times for appointments are kept to agreed timescales. use CRM systems to create and
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applicants with the opportunity to undertake an original multi-disciplinary impactful programme of applied research by working with heart failure patients, their clinical carers, and academic researchers. Core
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access to our employee assistance programme Join a supportive, ambitious PA community and make a meaningful contribution to the success of our University. Your organisational skills, attention to detail
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wider skills ecosystem. About the role Reporting directly to the Director of Apprenticeships, you will set the vision for business development and lead a multi‑disciplinary team covering new programme
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, chemists and computational experts pioneering research using an advanced, optogenetically controlled human-based nerve-muscle model. Using functional, molecular and metabolomics technologies, integrated with
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on understanding what is essential for multimodal learning when computation, memory, or energy are limited. Rather than scaling up models, the research aims to identify principled, lightweight methods