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Research Associate, Probabilistic Program, Symbolic Neuro - Symbolic (AI)

The Alan Turing Institute

Location: London
Salary: £42,893 to £48,510 (depending on experience) plus excellent benefits
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 4th December 2023
Closes: 7th January 2024
 

HOURS 36

The Alan Turing Institute

Named in honour of Alan Turing, the Institute is a place for inspiring, exciting work and we need passionate, sharp, and innovative people who want to use their skills to contribute to our mission to make great leaps in data science and AI research to change the world for the better.

Please find more information about us here

BACKGROUND

In 2022, the Alan Turing Institute signalled its intention to establish a portfolio of foundational AI research, which would complement the strengths of the institute around applications of AI and AI policy. The initial portfolio of research contains projects in two themes around foundation models and game theory. Five projects within a new probabilistic programming theme are being launched in early 2024. This role is being recruited to be part of one of the new projects in the probabilistic programming theme.

The Probabilistic Program Scaffolds for LLMs project aims to improve the reliability and consistency of large language models (LLMs) by integrating the LLMs with probabilistic answer set programming (P-ASP). This will allow for enforcement of logical, semantic, and syntactic constraints on LLM outputs. This role will involve developing methods to map between natural language and P-ASP frameworks, designing experiments to analyse LLM reasoning capabilities, developing methods for implementing P-ASP solvers to reformulate prompts, and develop strategies for constrained inference in LLMs.

CANDIDATE PROFILE

The successful candidate will focus on evaluating and enhancing novel methods using answer set programming (ASP) to improve the mathematical and logical reasoning capabilities of large language models (LLMs).

DUTIES AND AREAS OF RESPONSIBILITY

  • Develop novel methods that apply declarative frameworks using the candidate's expertise in ASP, probabilistic programming, and symbolic AI to LLMs.
  • Lead projects on scaffolding LLMs with logic-based constraints using ASP and probabilistic programming.
  • Design experiments and benchmarks to analyse LLM reasoning capabilities using logic-based scaffolds.
  • Collaborate to define research directions and implement objectives focused on neuro-symbolic integration

Person Specification

  • A PhD or equivalent qualification/experience in Mathematics, Computer Science, or a closely related discipline.
  • A solid background in probabilistic programming, logic programming or symbolic models for artificial intelligence. Strong mathematical and logical reasoning abilities.
  • Hands-on experience with at least one of the following topics: ASP, Neural logic programming, inductive logic programming.

Please see our portal for a full breakdown of the Job Description.

Terms and Conditions

This full-time post is offered on a 2-year fixed-term basis starting in March 2024. The annual salary is £42,893 - £48,510 (depending on experience) plus excellent benefits, including flexible working and family friendly policies, https://www.turing.ac.uk/work-turing/why-work-turing/employee-benefits

The Alan Turing Institute is based at the British Library, in the heart of London’s Knowledge Quarter. We expect staff to come to our office at least 4 days per month. Some roles may require more days in the office; the hiring manager will be able to confirm this during the interview.

Application procedure

Please see our jobs portal (via the ‘Apply’ button above) for full details on how to apply and the interview process.

Equality Diversity and Inclusion

We are committed to making sure our recruitment process is accessible and inclusive.

This includes making reasonable adjustments for candidates who have a disability or long-term condition. Please contact us at adjustments@turing.ac.uk to find out how we can assist you.

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