Research Scientist – Control & Machine Learning
This is a research role focused on getting learning and control methods to work on real heavy industrial plants. The work covers modelling plant behaviour from noisy data, optimising under uncertainty, and moving methods into production alongside engineers. A strong candidate has deep research experience in control or machine learning, writes solid Python, and cares about how methods perform in a live plant.
How it fits the AI era
The role sits at the point where machine learning meets physical systems, with methods like Bayesian optimisation, model based reinforcement learning and physics informed models guiding real equipment. AI can help find patterns in plant data and suggest better actions, but judgment about physical limits, safety and what a failure means still needs people. Talking with operators and process engineers stays a human skill that shapes which problems are worth solving.
How it builds AI experience
Someone in this role would gain hands on experience applying learning based control to real manufacturing plants, from early research through to deployment and follow up on results. It also means learning how to transfer models between sites and learn while controlling. To build this ahead of time, try open source projects in system identification or Bayesian optimisation using simulated industrial processes.
Worth showing
- Control or system identification research
- Python and PyTorch implementation
- Deployed ML or control algorithms
- Rigorous experiments and baselines
This is our own read, written from the employer’s posting and the company’s Spotlight. The employer’s posting is the source of truth for the role.
About Gigaton
Industrial artificial intelligence platform providing self learning process control to cut fuel costs and carbon emissions in energy intensive manufacturing.
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