Reinforcement Learning Researcher

Applied Computing is focused on building Orbital, a physics-informed foundation model for energy operations. The Reinforcement Learning Researcher will own the development of learning-based optimization systems and ensure safe and efficient policy learning frameworks for industrial processes.

Responsibilities:

• Design & Implement RL-Based Decision Systems
• Process optimisation (yield, efficiency, cost reduction)
• Control policy learning (setpoint optimisation, constraint handling)
• Sequential decision-making under uncertainty
• Work across:
• Model-free RL (policy gradients, actor-critic, offline RL)
• Model-based RL (world models, planning-based methods)
• Hybrid approaches combining RL with optimisation / MPC
• Build Physics-Constrained RL Systems
• Embed domain knowledge into policy learning:
• Hard constraints (safety, operating limits, regulatory bounds)
• Soft constraints (efficiency, degradation, economic trade-offs)
• Physics-informed reward shaping and transition models
• Ensure policies:
• Respect physical feasibility
• Generalise across operating regimes
• Remain stable under real-world disturbances
• Offline RL, Simulation & Digital Twin Integration
• Develop RL systems that work in data-scarce and risk-sensitive environments:
• Offline RL from historical plant data
• Simulation-based training via digital twins
• Sim-to-real transfer strategies
• Handle:
• Distribution shift
• Partial observability
• Sparse / delayed rewards
• Safety, Robustness & Interpretability
• Design safe RL systems for production environments:
• Constrained RL / safe exploration
• Policy validation before deployment
• Fail-safe mechanisms and fallback strategies
• Ensure outputs are:
• Interpretable to engineers and operators
• Auditable and explainable
• Reliable under sensor faults and regime changes
• Production-Grade Deployment
• Deploy RL systems into real-world infrastructure:
• Containerised deployment (Docker, AWS / Azure)
• Integration with control systems (APC, DCS, advisory layers)
• Real-time inference and monitoring
• Build pipelines for:
• Continuous policy evaluation
• Safe rollout and rollback
• Online / batch policy updates
• Benchmarking & Validation
• Define evaluation standards for RL systems:
• Offline policy evaluation
• Counterfactual analysis
• Comparison vs MPC, heuristics, and operator baselines
• Ensure:
• Measurable economic impact
• Reproducible results
• Defensible performance claims