45 cloud-computing-"https:"-"https:"-"https:"-"UCL" positions at Manchester Metropolitan University
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
cloud infrastructure using Microsoft Azure and/or Amazon Web Services. Strong hands-on expertise with core cloud services across Microsoft Azure and AWS, including networking, storage, compute, security
-
Programme Cycle to Work scheme Various other rewards and benefits. How To Apply: Your application must include a supporting statement which provides examples of how your experience and skills match each
-
technology sector—we recently achieved TEF Gold and an OFSTED Outstanding rating. The Department of Computing and Mathematics is a thriving academic community of over 80 staff and 2,500 students. We have
-
, maternity cover position. Key Skills & Experiences The ideal candidate will have a background in customer service, and experience managing a Finance and Procurement System. Experience with Oracle Cloud and
-
Process Automation (RPA) capability uses Blue Prism Cloud to create digital workers that automate repetitive, rule-based tasks, such as the determination of Fee Status for new applications
-
to deliver our full suite of applications, mainly on Microsoft and Azure cloud technologies. This is an exciting role within the University, and you will have the opportunity to develop many new skills whilst
-
programme, an exciting opportunity to shape the future of cyber resilience at Manchester Met. About the Role We're looking for a skilled Cyber Security Analyst to help protect our diverse digital estate and
-
this exciting area of IT delivery. Essential Skills: Experience of managing cloud-hosted phone platforms (e.g. Gamma's SIP Trunk Call Manager, Horizon, and Horizon Contact Centre) Experience of using Putty and
-
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
-
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