Research Fellow in Natural Language Processing Ref 8587

School/department: Engineering and Informatics
Hours: full time or part time hours considered up to a maximum of
1.0 FTE. Requests for flexible working options will be considered (subject to business need).
Contract: fixed term until 31 October 2023
Reference8587
Salary: starting at £34,304 to £40,927 per annum,pro rata if part time
Placed on: 10 June 2022
Closing date30 June 2022. Applications must be received by midnight of the closing date.
Expected Interview date: TBC
Expected start date: TBC


Job description

The Artificial Intelligence Research Group at the University of Sussex is looking for a Research Fellow to work on two projects: the Innnovate UK funded project MeeTooSaives; and the Horizon 2020 funded project eCharge4Drivers. 

MeeTooSaives is a collaboration with MeeToo Education Ltd and Bristol University Medical School.  The MeeToo app delivers free, safe, anonymous, pre-moderated, peer support to 56k children and young people in the UK.  The MeeTooSaives project aims to integrate Machine Learning and Natural Language Processing into the app in order to interrogate all data, rather than a specifically high-risk subset, to identify risk patterns, analyse semantic content and predict risk.  Crucially, NLP will be used to extend support options, but integrating smart links to topic-matched resources.  This is a research-intensive role with high publication potential in the rapidly developing field of humane and ethical Articificial Intelligence. 

eCharge4Drivers is an EU-funded project that aims to improve the Electric-Vehicle charging experience. Our contribution to this large project concerns an analysis, using NLP methods, of online discussions relating to electric vehicle charging on various social media platforms.

Key responsibilities

On the MeeTooSaives project, the team at the University of Sussex is responsible for delivery of the Smart Links work package.  In this work package, the objective is to apply advanced NLP/ML techniques to identify useful resources, based on the semantic content of users’ posts/posting profile/similarity to other users.  You will work with a full-time Data Scientist seconded from MeeToo to the University of Sussex in order to deliver these objectives.  On the eCharge4Drivers project, the team at the University of Sussex is responsible for the collection and analysis of a large dataset concerning electric vehicle charging. This data will use our in-house software to discover and collect and analyse data on several social media platforms including Twitter, Facebook, Instagram, YouTube and Reddit.  

On both projects, your role will be to advise on the likely effectiveness of different approaches, carry out analysis and evaluation of methods based on data available data and prototype innovative solutions. Your role will involve the implementation cutting-edge academic solutions for relevancy matching, using deep neural networks applied to language, and their application in this domain to demonstrate feasibility and deliver a proof-of-concept.  You will need to document and characterising the advantages and disadvantages of each method. As part of the role, you will be required to write scientific papers and monthly reports.  On MeeTooSaives you will also participate in quarterly consortium meetings and contribute support on other work packages as required.  For example, in the Enhanced Peer-Support work package, AI is being deployed to monitor posts and predict/identify risk and in the Concurrent user-centre evaluation work package, led by the University of Bristol, id-depth qualitative research is being carried out in the the acceptability of newly designed and implemented practical innovations from the other work packages.   User evaluation will be a critical driver in the development of practical in-domain solutions for relevancy matching.

Key Requirements. This post is suited to a highly motivated individual with excellent scientific and technical skills, and a willingness to publish in high profile venues. The candidate should have significant experience in natural language processing, machine learning and knowledge representation as well as a willingness to operate in a dynamic research environment within an international team.

Candidates should have a PhD in computer science or an equivalent field, combined with a strong background in natural language processing and machine learning. The candidates should have demonstrable experience in python programming and knowledge of various libaries used by the community e.g., pytorch, tensorflow and huggingface.

Background.The AI Research Group at the University of Sussex was created through the merger of the Evolutionary and Adaptive Systems (EASy) and the Data Science groups. We conduct research and teach a wide range of AI related topics, often cross-disciplinary and with a unique Sussex angle. We collaborate internationally and locally with academics, businesses and public stakeholders, working at the frontiers of knowledge, on solving real-world problems, and in policy and public outreach.  Dr Weeds co-directs The Data Intensive Science Center, University of Sussex (DISCUS) with Prof Oliver in the School of Mathematics and Physics. DISCUS is concerned with the application of modern Data Science and Machine Learning to real world problems and research in other disciplines.

Advantages and career development. This position is ideally suited for somebody who wants to advance their career in the rapidly expanding field of natural language processing.  This project will provide an opportunity to publish and present in high-impact journals and conferences.  The project has the potential to lead to a spin-off, further industrial collaboration and broad networking opportunities.

Please contact Dr Julie Weeds (j.e.weeds@sussex.ac.uk) or Professor David Weir (d.j.weir@sussex.ac.uk) for informal enquiries.

The University is committed to equality and valuing diversity, and applications are particularly welcomed from women and black and minority ethnic candidates, who are under-represented in academic posts in Science, Technology, Engineering, Medicine and Mathematics (STEMM) at Sussex. 

The University of Sussex values the diversity of its staff and students and we welcome applicants from all backgrounds.

You can find out more about our values and our EDI Strategy,  Inclusive Sussex, on our webpages.

Download job description and person specification Ref 8587 [PDF 229.00KB]

The University requires that work undertaken for the University is performed from the UK. 

Visa Sponsorship Queries:

  • This role has been assigned an eligible SOC code and meets the salary requirements for Skilled Worker Sponsorship if full time. Please consult our Skilled Worker Visa information page  for further information about Visa Sponsorship.
  • Please note that this position may be subject to ATAS clearance if you require visa sponsorship.

How to apply

Download our academic application form [DOC 199.50KB] and Personal Details and Equal Opportunities Form [DOC 119.50KB] 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 in PDF format (we are unable to accept applications as google.docs or .pages) 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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Post Title: Research Fellow in Natural Language Processing

School/department: Engineering and Informatics
Hours: full time or part time hours considered up to a maximum of
1.0 FTE

Requests for flexible working options will be considered (subject to business need).
Contract: fixed term until 31/10/2023 
Reference

Salary: starting at £
33,797 to £40,322 per annum,pro rata if part time

Placed on: 10 June 2022

Closing date30 June 2022. Applications must be received by midnight of the closing date.
Expected Interview date:
TBC
Expected start date:
TBC