Knowledge Transfer Partnership (KTP) Associate Ref 3180

School/department: School of Engineering and Informatics 
Hours: Full time 
Contract: Fixed term (30 months) 
Reference:  3180
Salary: £35,550 - £42,418 per annum
Placed on: 26 April 2018
Closing date:  28 May 2018.  Applications must be received by midnight of the closing date.
Expected interview date: 8 June 2018
Expected start date: 01 August 2018


Job description

Moogsoft Limited and the University of Sussex together offer an Associate position for developing a mathematical framework and a computational implementation for the efficient and scalable calculation of metrics associated with the logical and physical graphs inherent in IT infrastructures. This post has been created as a result of a Knowledge Transfer Partnership between the University of Sussex and Moogsoft. The Associate will lead this 30-month project, working with staff at Moogsoft and the School of Engineering and Informatics.

The Knowledge Transfer Partnership scheme is a UK-wide programme that has been helping businesses for the past 40 years to improve their competitiveness and productivity through the better use of knowledge, technology and skills that reside within the UK Knowledge Base (http://ktp.innovateuk.org).

The KTP Associate will be primarily based at Moogsoft Limited (Kingston offices) but also spend time at the University of Sussex. The KTP Associate will investigate existing implementations of distributed and parallel algorithms to calculate graph metrics and assess their potential applicability to Moogsoft’s system. In close collaboration with the science and core engineering teams at Moogsoft and the Academic Supervisors, the KTP Associate will design and implement a distributed framework for the efficient calculation of graph metrics that are relevant to Moogsoft’s operations. They will explore the applicability of state-of-the-art distributed and parallel processing frameworks, such as Spark and GraphX as well as CUDA by NVIDIA. Cutting edge research on distributed graph processing will be also taken into account in the design and implementation of the computational framework. An integral part of this project is the extensive experimental evaluation of the developed framework using real and synthetic datasets. Limitations or bottlenecks identified throughout the evaluation phase will be dealt with in subsequent development phases following an agile approach. The KTP Associate will also investigate the applicability of optimisations in the distributed calculation of graph metrics e.g. by optimally partitioning graphs for distributing computation over a large number of physical servers or by allowing for partial calculations of metrics that can be performed and then aggregated in real-time.

For informal enquiries, please contact Dr George Parisis (G.Parisis@Sussex.ac.uk), Dr Luc Berthouze (L.Berthouze@sussex.ac.uk) and Dr Robert Harper (rob@moogsoft.com).

Download the full job description and person specification Ref 3180 [PDF 152.76KB]


How to apply

Download our academic post application form [DOC 301.50KB] and personal details and equal opportunities form [DOC 162.50KB] and fill in all sections.

Email your completed application, and personal details and equal opportunities form, to enginfrecruitment@sussex.ac.uk 

You should attach your application form and all documents to the email (don't use a web-based upload/weblink service) and use the format job reference number / job title / your name in the subject line.

You can also send your application by post to Human Resources Division, Sussex House, University of Sussex, Falmer, Brighton, BN1 9RH.


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