Time Series Researcher

At Applied Computing, we’ve built Orbital, a physics-grounded multi-agent AI copilot that operates directly inside heavy industrial systems such as refineries, upstream assets, and energy-intensive plants. Orbital fuses real-time sensor data, physics-based models, and domain-trained language models to deliver interpretable predictions, anomaly detection, and optimisation recommendations in live production environments.

Time Series Researcher owns the core of Orbital’s temporal intelligence. This role exists to design, validate, and deploy foundational time-series models that operate under real world constraints: noisy sensors, partial observability, physical laws, and high economic stakes. This is not offline research. You will own the full lifecycle; from theoretical formulation and experimentation to real-time inference, uncertainty estimation, and continuous retraining in production.

What You’ll Own

• Orbital’s foundational time-series modelling stack
• Physics-informed and probabilistic model design
• Uncertainty quantification and robustness under sensor faults
• Research → production translation for time-series models
• Benchmarking standards and validation protocols used across the company

Job Requirements

Must-Have Qualifications

• PhD in Computer Science, Statistics, Applied Mathematics, Physics, or related field
• First-author publications in time-series modelling, forecasting, signal processing, or physics-informed ML
• 3+ years of hands-on research experience in time-series or sequence modelling
• Strong foundation in:
• Deep Learning
• Probabilistic modelling
• Expert Python skills with production-grade PyTorch code
• Experience deploying ML models into real systems

How We Work

• Research is judged by production impact, not paper count
• We value principled models that survive contact with reality
• We iterate aggressively, benchmark honestly, and ship responsibly
• Physics, statistics, and learning are treated as complementary, not competing

What This Role Is Not

• Not offline academic research disconnected from deployment
• Not pure deep-learning experimentation without domain grounding
• Not feature engineering on static datasets
• Not a support role; this position owns core IP

Job Responsibilities

• Core responsibilities:
• Design & Implement Foundational Time Series Models
• Design core time-series architectures supporting: forecasting, classification / anomaly detection, optimisation & control-adjacent tasks
• Explore and select appropriate objectives examples: probabilistic losses, generative formulations, reinforcement-learning-inspired objectives where appropriate
• Develop hybrid approaches that blend: classical statistical models, deep learning architectures, physics-based constraints
• Embed Physics – Informed Structure
• Integrate domain physics into learning systems, including: conservation laws, process constraints, differential-equation-based priors
• Improve generalisation, interpretability, and extrapolation beyond training regimes
• Ensure models respect physical feasibility in production settings
• Uncertainty, Robustness & Reliability
• Design uncertainty-aware models (Bayesian, ensemble, hybrid)
• Quantify confidence under sensor drift and failure, regime change, sparse or delayed ground truth
• Ensure outputs are usable by operations and engineering teams, not just statistically elegant
• Production Structured AI Code
• Containerise and deploy models using Docker on AWS / Azure (EKS, ECS, SageMaker)
• Build or integrate CI/CD pipelines for training, evaluation, rollout and rollback, automated retraining triggers
• Benchmarking & Validation
• Define rigorous back-testing and evaluation protocols
• Build automated benchmarking pipelines across datasets, regimes, and failure modes
• Compare against classical baselines and modern deep-learning approaches
• Ensure claims are defensible to customers, partners, and internal stakeholders

What Success Looks Like

• First 90 Days
• Deep understanding of Orbital’s data, domains, and production constraints
• Contribution to at least one core time-series model or experimental track
• Clear ownership of a modelling problem with defined success metrics
• 6–12 Months
• One or more foundational models running reliably in production
• Demonstrable improvements in forecast accuracy, robustness under faults, and uncertainty calibration
• Models actively used by downstream agents and optimisation layers
• Benchmarking standards adopted across the research team

Job Benefits

• Remote or hybrid role with an office in Fitzrovia
• Competitive salary
• Attractive set of benefits