Mathematics of AI Research Associate

About the Project

TODS Ventures is developing a highly ambitious Enterprise AI Governance Platform — a research-led project at the intersection of artificial intelligence, mathematics, law, ethics, and enterprise technology. The platform applies rigorous, multi-dimensional governance to AI interactions  project combines original academic research with practical software engineering, and is preparing for peer-reviewed publication alongside a commercial product launch. This is an early-stage, high-calibre founding team engagement.

We are looking for a strong technical academic or research scientist with a background in Mathematics, Physics, or Computer Science — at MSc or PhD level. You will serve as a core intellectual contributor to the project's mathematical and AI foundations, working directly with the CEO/CTO to formalise the platform's theoretical basis, review quantitative approaches, and co-author peer-reviewed papers. Deep fluency in linear algebra, vector spaces, statistics, probability, and AI is central to the role.

This is a fractional advisory engagement at approximately 30% FTE, with flexibility on scheduling.

• Provide mathematical and theoretical guidance on the core platform architecture — with a strong focus on linear algebra, vector spaces, metric spaces, and multi-dimensional decision frameworks.
• Co-author and review peer-reviewed research papers targeting top AI, ML, and fairness venues.
• Advise on statistical and probabilistic modelling approaches — including drift detection, composite scoring, and distributional analysis across large interaction datasets.
• Review and validate quantitative methods, proofs, and mathematical formal isms embedded in the platform design.
• Contribute to the academic research agenda — helping to frame open questions, select methodologies, and position contributions within the existing literature.
• Engage with relevant prior work in areas such as knowledge graph embeddings, statistical learning theory, multi-criteria decision analysis, and AI safety.

• Degree in Mathematics, Physics, or Computer Science at MSc or PhD level — with strong quantitative foundations.
• Deep fluency in linear algebra and vector spaces — as applied to computational or AI problems.
• Strong grounding in statistics and probability — modelling, inference, and distributional reasoning.
• Solid understanding of artificial intelligence and machine learning, including familiarity with large language model research.
• Experience translating mathematical theory into computational approaches or engineering designs.
• Ability to engage constructively with a small, fast-moving research and engineering team.

• A PhD in a relevant discipline, or an active research profile with peer-reviewed publications in AI, ML, or mathematics.
• Background in knowledge graph embeddings, geometric deep learning, or representation learning.
• Familiarity with multi-criteria decision analysis (MCDA) or social choice theory.
• Interest in or prior work on AI fairness, accountability, transparency, or ethics.
• Experience with drift detection, distribution shift, or statistical process control.
• Comfort with information theory, formal logic, or applied optimisation.
• Prior engagement with industry AI projects alongside academic research.

Type Freelance Advisory

Duration 1 Jul 2026 – 31 Mar 2027

Commitment Part-time · ~30% FTE

Location UK or EU
· Remote / Hybrid

Rate To be negotiated