Algorithm Developer (KTP Associate)

The candidate will develop an algorithm for gas emission localisation and quantification using advection-diffusion models, CFD, and Navier-Stokes equations. Working alongside Durham.

Key tasks will include:

• Integrating sensor data with gas transport models for improved detection.
• Developing numerical methods to enhance prediction accuracy.
• Collaborating with MIRICO’s Digital Team to optimise performance.
• Validating models against experimental data and refining as needed.

The candidate is expected to carry out research at the highest standard and will be supported to publish at conferences and journals. The results of the work will be embedded in MIRICO’s solutions. Their work will directly contribute to MIRICO’s solutions, driving innovation in gas emission monitoring.

Person Specification

Qualifications

Essential

• A good first Degree, and a PhD (or equivalent) in applied mathematics, statistics or a related subject

Experience

Essential

• Actively involved in at least one project that delivered specific outcomes into a commercial organisation
• Minimum 2 years academic and/or commercial experience of scientific or statistical modelling
• Developing scientific solutions in Python, including selection and use of libraries
• Processing large data sets, data pipelines, algorithm optimisation
• Computational Fluid Dynamics (CFD) modelling and simulations
• Presenting scientific findings to technical and non-technical audiences
• Formal academic and report writing of a quality commensurate with higher education qualifications

Desirable

• Collaboration projects with academic/industry colleagues for software development
• Producing research publications in journals and conferences
• Familiarity with Agile teams and processes
• Presenting research findings at national/international venues
• Advanced gas transport modelling, particularly for environmental simulations, gas dispersion, or emissions forecasting

Skills

Essential

• Ability to work autonomously, organising and planning own workload against the project plan, with excellent time-management skills
• Demonstrable ability to work cooperatively as part of a team
• Demonstrable problem-solving skills
• Ability to propose and apply novel (literature based) and innovative ideas for solving a problem

Desirable

• Knowledge of Bayesian uncertainty techniques, including emulation, history matching or equivalent calibration techniques
• Ability to attract collaboration and opportunities for the project
• Ability to plan and manage independent research
• Knowledge of Python visualisation libraries – Bokeh, Holoviews
• SQL, Postgres and SQLAlchemy
• Familiarity with software process – coding standards, unit tests and source control

The KTP Associate will lead a Knowledge Transfer Partnership (KTP) project that is a collaboration between Durham University and MIRICO, based in Oxfordshire. The Knowledge Transfer Partnership (KTP) scheme helps businesses to innovate and grow through the aid of discipline specific academic expertise. It does this by linking them with an academic supervisory team and a KTP Associate in a university to work on a specific project.

Working alongside the Digital Team at MIRICO, the Algorithm developer will develop a new algorithm to enhance the localisation and quantification of gas emissions. This will involve leveraging advanced gas transport models, including advection-diffusion equations, computational fluid dynamics (CFD), and Navier-Stokes equations, to provide deeper insights into emission sources and dispersion patterns.