Time Series Researcher

We need to output HTML fragment with sections: About the role? Actually we have "About Applied Computing" description, then "The Role". We need to use

for section headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We should not repeat job title. We need to include sections only if posting has real specific content. The posting includes description of company, role overview, responsibilities (What You'll Own, Design & Implement..., etc.), qualifications (Must-Have Qualifications), maybe skills (Deep Learning & Probabilistic modelling, Expert Python, etc.), maybe benefits? Not mentioned. Pay? Not mentioned. Schedule? Not mentioned. So we include sections: About the role? Actually "About Applied Computing" is company description, not role. Could be "About the role" heading for role overview. We'll put that. We need to use

• for any list of duties/requirements/benefits. So for responsibilities we can list bullet points. For qualifications list bullet points. For skills maybe list bullet points. We must preserve all substantive information, not invent or summarize away detail. Remove boilerplate etc. We need to output only HTML fragment, no /, no markdown fences. Let's craft:

  About the role

  ... description of role overview ...

  Then responsibilities: maybe

  Responsibilities

  with
  • items. Qualifications:

    Qualifications

    with
    • . Skills: maybe

      Skills

      with
      • . We must not create sections like Benefits, Pay, Schedule if not present. We need to ensure we include all substantive info: company mission, founding, Orbital description, funding, etc. That could go under About the role? Or maybe a separate section "About Applied Computing"? But headings limited to given list. Could put company description under About the role as part of role overview. Or we could add a

        before responsibilities. We must not repeat job title. The posting didn't explicitly give a job title besides "Time Series Researcher". We should not repeat it as heading. Let's extract content: Company description: - Applied Computing founded 2024 to build Orbital, a physics-informed foundation model for energy operations. - Live across oil and gas, refineries, petrochemicals. - Mission: sustainable abundance for a growing planet. - Hydrocarbon industry keeps world running but complexity leaves operators tied to legacy systems, using About the role

containing company description and role overview.

Responsibilities

containing bullet lists for duties (What You'll Own, Design & Implement..., Embed Physics-Informed Structure, Uncertainty..., Production Structured AI Code, Benchmarking & Validation, How We Work, What This Role Is Not, What Success Looks Like). Might be many bullets; we can group.

Qualifications

containing Must-Have Qualifications list.

Skills

containing Deep Learning & Probabilistic modelling, Expert Python skills with production-grade PyTorch code, Experience deploying ML models into real systems, Experience with Docker on AWS/Azure (EKS, ECS, SageMaker), CI/CD pipelines, etc. But some of those are also in responsibilities; we should avoid duplication? The instruction: preserve ALL substantive information; do not invent or summarize away detail. Duplication is okay if it's in the posting; but we should not repeat same content unnecessarily? It says remove duplicated lines. So we should avoid duplicating identical lines. However some content appears both in responsibilities and skills; we need to decide where to place each piece without duplication. Let's map: Responsibilities bullet points: - 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 - 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 - 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 - 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 - 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 - 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 - 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. - 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. - 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, Uncertainty calibration; Models actively used by downstream agents and optimisation layers; Benchmarking standards adopted across the research team 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 - Demonstrated experience in Deep Learning & Probabilistic modelling - Expert Python skills