Open to senior R&D and algorithm engineer roles.
UK · remote or on-site · £150k+ · immediate · eligible to apply for UK Global Talent Visa
Doctorate at Cranfield University's IVHM Centre defined two contributions to multi-objective sensor optimisation — MOSOF and the Normalised Diagnostic Contribution Index (NDCI) — across three first-author papers in Sensors (MDPI). The first product application of that work is AcoustR, live with early customers. Underneath both, five years founding and running an industrial-automation business serving state-run oil & gas — full-stack ownership of high-voltage control systems, algorithm design, and customer delivery — which is the operational reality that grounds the research.
Five reasons in plain English.
PhD-level optimisation research translated into working code, runnable Python frameworks, and venture prototypes — not slide decks.
Three peer-reviewed papers in Sensors (MDPI) on sensor optimisation and diagnostic contribution scoring — methods you can read and verify before the first interview.
Five years as founder and director of a high-voltage / industrial-automation company before the PhD. Operates between mathematical method, software implementation, and commercial constraint.
Most useful where model accuracy, cost, latency, reliability, and operational risk must all be traded off simultaneously — not collapsed into a single metric.
Aerospace IVHM, multi-objective genetic algorithms, sensor engineering, acoustic diagnostics, industrial automation, and applied software — in one person, without simulation-only depth.
Three ways to engage.
Applied research → working prototype
For teams converting mathematical methods into validation pipelines, decision frameworks, and engineering tools. Strongest fit where the problem has a rigorous formulation that existing tools do not yet implement.
Optimisation, ranking, and scoring logic
For teams needing optimisation, multi-criteria ranking, sensor/data scoring, diagnostic contribution logic, or selection algorithms. Can design the method, implement it in Python, and document it for the wider team.
Architecture, velocity, and commercial judgement
For companies that need someone who can design architecture, build fast, reason commercially, and own a technical domain end-to-end from day one. Prior founder background means I understand the context, not just the code.
What I can contribute quickly.
Understand the existing technical architecture, the live trade-off decisions the team faces, and where current tooling is failing or absent. Ship at least one useful thing — a working prototype, a scored analysis, or a documented method — before the first month is out.
Own a domain. Apply the optimisation and diagnostic methods to the team's hardest open problem — not as a study but as a deliverable with a recommendation attached. Establish working relationships with the adjacent engineering, data, and product leads.
Propose the next set of problems worth solving and the methods to solve them. Operate independently enough that the team doesn't need to manage me — only align on priorities.
Three shapes of role.
All three sit at the senior-IC bracket. Happy to be the most senior IC on a small team, or the deepest specialist on a larger one. Engineering-management interest in the right shop, but not the primary target.
Senior R&D Engineer
- Multi-objective optimisation & Pareto-front analysis
- Multi-objective genetic algorithms (NSGA-II)
- IVHM, prognostics, fault detection & isolation
- Signal processing, sensor fusion, condition monitoring
- Aerospace + industrial domains; HIL validation
Senior Algorithm Engineer
- Genetic algorithms; information-theoretic scoring (NDCI)
- ML pipelines — scikit-learn, pandas, NumPy / SciPy
- Python and C++ delivery; MATLAB / Simulink for modelling
- Reproducibility: nested cross-validation, deterministic seeds
- Open-source: three MOSOF / NDCI reference repos shipped
Founding / Principal Engineer
- 5 yrs as founder & lead engineer (SSL Elektrik-Elektronik)
- Full-stack ownership: hardware, firmware, software, customer
- Two live ventures: AcoustR (live), Sensorry (prototype)
- Cross-functional delivery with operators, utilities, EPC
- Hazardous-environment automation; 20% efficiency uplift
Three sectors, one method.
AI / ML scale-ups
For a model-plus-sensing team — perception, anomaly detection, predictive maintenance — I bring an information-theoretic discipline to what's worth measuring, not just what to do with the measurements. The MOSOF / NDCI work is exactly the loop you want before scaling a sensor-driven product to new platforms: it tells you which signals carry the diagnostic information you're paying for, and which ones you can drop without losing accuracy.
Aerospace primes & MROs
The doctorate is on aircraft health management, evaluated end-to-end on a B737-class cross-subsystem case (Engine, Fuel, EPS, ECS). For a fleet-readiness or PHM team, I can speak the right language with reliability engineers on day one — MTBF aggregation, fault-mode coverage, V-model validation, repeated nested cross-validation — and bridge it to the optimisation tooling without losing fidelity in translation.
Autonomous & robotics
Perception stacks are sensor-selection problems with extra steps. The work transfers directly: which sensor families to install on a new chassis, what acoustic / pressure / temperature channels add diagnostic value vs cost / weight / power, and how to make those trade-offs explicit to a hardware-or-firmware team rather than buried inside a black-box score. Comfortable shipping in C++ as well as Python when the platform demands it.