At a Glance
- Tasks: Lead cutting-edge research in reinforcement learning for real-world energy optimisation.
- Company: Join a pioneering AI company focused on sustainable energy solutions.
- Benefits: Competitive salary, innovative work environment, and opportunities for impactful research.
- Other info: Be part of a dynamic team driving real change in the hydrocarbon industry.
- Why this job: Make a difference in the energy sector with your expertise in AI and optimisation.
- Qualifications: PhD in relevant field and extensive RL research experience required.
The predicted salary is between 60000 - 80000 £ per year.
Applied Computing was founded in 2024 to build Orbital, a physics-informed foundation model for energy operations. We’re live across oil and gas, refineries, and petrochemicals, working towards our mission: sustainable abundance for a growing planet. The hydrocarbon industry keeps the world running. But its complexity has left operators tied to legacy systems, making critical decisions on less than 10% of available data. We built Orbital to change that. It’s a foundation model built specifically for energy that lets companies use AI at scale, harnessing all of their operational data and optimising in real time for any metric. Decisions get faster, operations get safer, and carbon intensity falls. We’ve raised over $32 million, including one of the largest seed rounds for an AI company in the UK. We’re just getting started.
What You’ll Own
- Orbital’s learning-based optimisation and control stack
- RL + control hybrid systems for industrial processes
- Safe and constrained policy learning frameworks
- Simulation environments and digital twin integrations
- Research → production translation for RL systems
- Benchmarking standards for decision-making systems
Requirements
Must-Have Qualifications
- PhD in Computer Science, Robotics, Control, Applied Mathematics, or related field
- First-author publications in:
- Reinforcement Learning
- Control systems
- Sequential decision-making
- 3+ years of hands-on RL research experience
- Strong foundation in:
- Reinforcement Learning (online + offline)
- Optimisation and control theory (MPC, dynamic programming, etc.)
- Deep learning (PyTorch)
- Experience with:
- Real-world deployment of ML systems
- Simulation environments or digital twins
- Working with noisy, real-world data
How We Work
- Research is judged by production impact, not paper count
- We optimise for real systems, not benchmarks alone
- We value safe, reliable decision-making over theoretical elegance
- Physics, control, and learning are treated as one system
What This Role Is Not
- Not toy RL environments (Atari, MuJoCo-only thinking)
- Not unconstrained policy learning without safety guarantees
- Not offline research disconnected from deployment
- Not a support role; this position owns core optimisation IP
Reinforcement Learning Researcher employer: Applied Computing Technologies
At Applied Computing, we pride ourselves on being an innovative employer that champions a culture of collaboration and real-world impact. Our commitment to employee growth is evident through our focus on cutting-edge research and the application of AI in the energy sector, providing unique opportunities for professional development in a rapidly evolving field. Located at the forefront of sustainable technology, we offer a dynamic work environment where your contributions directly influence the future of energy operations.
Contact Details:
Applied Computing Technologies Recruitment Team
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