Research Assistant or Fellow in Large Data Analytics and Large Language Models

Organisation

Cranfield University

Faculty/School/PSU/Department

Executive Office

Based at

Cranfield or Shrivenham, Oxfordshire

Hours of work

37 hours per week, normally worked Monday to Friday. Flexible working will be considered.

Contract type

Fixed term contract

Fixed Term Period

For 12 months

Salary

Research Assistant (if close to completing PhD): Full time starting salary is usually in the range of £28,203 to £32,039 per annum, with potential progression to £39,586 per annum; Research Fellow (if PhD obtained): Full time starting salary is usually in the range of £37,337 to £41,406 per annum

Are you looking for an opportunity to create significant impact and influence a new large data set model in parallel with a new Large Language Model (LLM)? We are offering a unique opportunity to drive the development and iterative improvement of both a new large dataset model and an associated LLM tool to generate informative report-based outputs.    

About the Role

Your role will be to contribute to the data capture, recording and iterative development of large data and the associated interrogation techniques, data visualisation through reporting and Large Language Model (LLM). 

You will be responsible for:

• The manual and computational capture and recording of large data sets, the design and integration techniques to enable data validation, integrity, analysis, output validation and reporting.
• Develop integrative strategies for a diverse set of data, integrating the outcomes to inform future projected trend analysis.
• Apply statistical and machine learning to project future data analysis.
• Manage and analyse large datasets using efficient data structures and providing infrastructure for sharing resources.
• Develop research objectives and proposals for own or joint research, with assistance of a mentor if required.
• Apply knowledge in a way which develops new intellectual understanding.  For example, generative AI, deep learning-based methods.

The responsibilities of this role are split between our Shrivenham and Cranfield sites. This post can be based at either, with travel to the other site as required.

About You

You will have extensive experience of working with large datasets and/or working with Large Language Models. Candidates are expected to have a history of developing models using large data sources and methods. You will have the ability to interact with academic and industry partners to explain complex ideas to non-scientists in a comprehensible way.

We value your ability to work independently and self-motivate, enabling you to find innovative and practical solutions to complex problems.

You will need to successfully undertake a Baseline Personnel Security Standard (BPSS) check to be offered the role. 

About Us

As a specialist postgraduate university, Cranfield’s world-class expertise, large-scale facilities and unrivalled industry partnerships are creating leaders in technology and management globally. Learn more about Cranfield and our unique impact here .

Our Values and Commitments

Our shared, stated values help to define who we are and underpin everything we do: Ambition; Impact; Respect; and Community. Find out more here .

We aim to create and maintain a culture in which everyone can work and study together and realise their full potential. We are a Disability Confident Employer and proud members of the Stonewall Diversity Champions Programme. We are committed to actively exploring flexible working options for each role and have been ranked in the Top 30 family friendly employers in the UK by the charity Working Families . Find out more about our key commitments to Equality, Diversity and Inclusion and Flexible Working here .

Working Arrangements

Collaborating and connecting are integral to so much of what we do. Our Working Arrangements Framework provides many staff with the opportunity to flexibly combine on-site and remote working, where job roles allow, balancing the needs of our community of staff, students, clients and partners.

How to apply                                   

For an informal discussion about this opportunity, please contact Christopher Buckland, Security Commercial Director, on (E): [email protected] .

Please do not hesitate to contact us for further details at [email protected] .  Please quote reference number 4886.

Closing date for receipt of applications:  18 August 2024