Research Scientist - Control & Machine Learning

About this role

At Gigaton, we’re on a mission to cut gigatonnes of carbon emissions from the world’s biggest emitting industries (like cement, steel and glass), by building autonomous AI control and optimisation systems that learn and leverage the physics of manufacturing. Our products run heavy industrial plants more efficiently, more stably, and with lower emissions in real time - laying the foundation for the next industrial revolution.

We are a team of scientists, engineers, builders, and operators who love hard problems, have high standards, and want to make change happen in the physical world. We care about deep tech, but we care even more about whether it delivers cost and carbon impact in a live plant, with real people, under real constraints. With Gigaton, you’ll solve really tough problems in places few people ever get close to, and build something that actually helps the planet.

Are you up for the challenge? Research questions you could initially work on Learning dynamics from operational data. How can we identify useful models of a plant from noisy, partially observed data collected during closed-loop control? Optimising under uncertainty. How can Bayesian optimisation improve plant performance when experiments are expensive, samples are noisy, and operating constraints must be respected? Learning while controlling.

How can we use dual control and active learning to choose actions that improve performance while gathering information about uncertain dynamics? Learning across plants. Which parts of a model or control strategy can transfer between plants, and how can we adapt them efficiently to a new site? These are meant as examples rather than an exhaustive list. We’re looking for someone who can bring depth to a relevant area, identify promising connections, and help us decide which problems to tackle next.

Your main responsibilities As a Research Scientist, you’ll: Help shape our research and development agenda, turning challenges from live plants into well-defined research problems. Develop and implement methods for modelling, identification, optimisation and control of complex physical systems. Design rigorous experiments, establish strong baselines and understand when and why a method works. Work with process engineers to incorporate plant physics, operating knowledge and constraints into models and algorithms.