Research Assistant

Keele University; United States; Remote; USD 56.000 - 74.000; Vollzeit

Zusammenfassung

Keele University is seeking a motivated team member to join the School of Computer Science and Mathematics to develop an open multimodal AI compliance and adversarial auditing benchmark for evaluating AI systems in regulated sectors.

The post holder will design and implement benchmark tasks across text, structured/telemetry, audio and vision-language data, and develop LLM evaluation pipelines including robustness testing.

Qualifikationen

• Master's degree in Artificial Intelligence, Machine Learning, Data Science, Computer Science, Cyber Security or a closely related area.
• Strong programming skills, particularly in Python, and practical experience with ML and/or LLMs/agentic AI.
• Experience with PyTorch, Hugging Face, multimodal AI, adversarial machine learning, LLM evaluation, LLM agents, cybersecurity, Git/GitHub or reproducible experimental pipelines would be desirable.

Aufgaben

• Develop and run LLM evaluation pipelines and implement adversarial robustness testing.
• Prepare and process multimodal datasets across text, telemetry, audio and vision-language data.
• Evaluate open-weight LLMs using reproducible experiments and contribute to the evaluation framework.

Kenntnisse

Python programming

ML/LLMs experience

Communication skills

Ausbildung

Master's degree in AI/ML/DS/CS/Cyber Security or related

Tools

PyTorch

Hugging Face

Git/GitHub

Jobbeschreibung

We are looking for a motivated team member to join a team of staff in Keele University/ The School of Computer Science and Mathematics.

The successful candidate will contribute to the development of an open multimodal AI compliance and adversarial auditing benchmark for evaluating AI systems in regulated sectors. The role will involve supporting the design, implementation and evaluation of benchmark tasks across text, structured/telemetry, audio and vision-language data.

The post holder will develop and run Large Language Model (LLM) evaluation pipelines, implement adversarial and robustness testing, prepare and process multimodal datasets, and evaluate open-weight LLMs using reproducible experiments. They will contribute to the shared evaluation framework, including robustness metrics, attack and perturbation testing, cross-modal evaluation and baseline model experiments. The candidate will also support the development of the project's compliance assessment component and contribute to documentation, open-source code, research outputs and the public release of the benchmark. The role will involve working closely with the academic team and the project's industry partner.

You will have a Master's degree in Artificial Intelligence, Machine Learning, Data Science, Computer Science, Cyber Security or a closely related area. You should have strong programming skills, particularly in Python, and practical experience with machine learning and/or LLMs/agentic AI. Experience with PyTorch, Hugging Face, multimodal AI, adversarial machine learning, LLM evaluation, LLM agents, cybersecurity, Git/GitHub or reproducible experimental pipelines would be desirable.

Keele University is renowned for its exciting approach to higher education, innovative research, beautiful campus, strong community spirit and excellent student experience. With a turnover in excess of £170 million, over 15,000 students and a total staff of approximately 2000, the University provides high-quality teaching across a wide range of academic and vocational subjects and promotes world-class research. Further information can be found at https://www.keele.ac.uk/about/

Keele University is committed to the principles of the Athena Swan charter, and values equity and diversity across our workforce. We strive to ensure that our workforce is representative of broader society, and therefore, we would actively welcome applications from women/men for this role.

Keele University values equity and diversity across our workforce and to ensuring our staff community is reflective of the diversity of our student population. In support of these commitments the University welcomes applications from individuals of Black, Asian and ethnic minority backgrounds for all roles.

Please note that the starting salary will typically be at the bottom of the pay scale shown above.

The University is committed to operating flexible working practices wherever possible.

The University