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 at scale. The 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.
The Role
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.
Key Responsibilities
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 formalisms 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.
Required Skills & Experience
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.
Desirable / Nice to Have
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
#J-18808-Ljbffr
