At a Glance
- Tasks: Design and deploy innovative time-series models for real-world energy operations.
- Company: Join a pioneering AI company focused on sustainable energy solutions.
- Benefits: Competitive salary, flexible work environment, and opportunities for impactful research.
- Other info: Dynamic team culture prioritising real-world impact over academic metrics.
- Why this job: Make a real difference in the energy sector with cutting-edge technology.
- Qualifications: PhD in relevant field and strong experience in time-series modelling.
The predicted salary is between 60000 - 80000 € per year.
About Applied Computing
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.
The Role
The 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
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
Time Series Researcher employer: Applied Computing Technologies
Applied Computing is an exceptional employer, offering a dynamic work environment where innovation meets real-world impact. As a Time Series Researcher, you will be at the forefront of developing cutting-edge models that drive sustainable energy operations, with ample opportunities for professional growth and collaboration in a mission-driven culture. Located in the UK, we provide a unique chance to contribute to a transformative project while enjoying a supportive atmosphere that values research with tangible outcomes.
Contact Detail:
Applied Computing Technologies Recruiting Team
StudySmarter Expert Advice🤫
We think this is how you could land Time Series Researcher
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend relevant events, and connect with potential colleagues on LinkedIn. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your time-series models and projects. This gives us a chance to see your work in action and understand how you tackle real-world problems, which is super important for roles like the Time Series Researcher.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. We recommend practising common interview questions related to time-series modelling and being ready to discuss your past research experiences. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our mission of sustainable abundance.
We think you need these skills to ace Time Series Researcher
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Time Series Researcher role. Highlight your relevant experience in time-series modelling and any publications you've authored. We want to see how your skills align with our mission at Applied Computing!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about the hydrocarbon industry and how your background in physics-informed ML can contribute to Orbital's goals. Let us know what excites you about this opportunity!
Showcase Your Projects:If you've worked on any projects related to deep learning or probabilistic modelling, make sure to showcase them. We love seeing real-world applications of your skills, so include links or descriptions that demonstrate your hands-on experience.
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It helps us keep track of applications and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at Applied Computing Technologies
✨Know Your Time Series Inside Out
Make sure you brush up on your time-series modelling knowledge. Be prepared to discuss your previous research, especially any first-author publications. Highlight how your work aligns with the practical applications of time-series models in real-world scenarios, particularly in energy operations.
✨Showcase Your Python Skills
Since expert Python skills are a must-have, be ready to demonstrate your coding abilities. Bring examples of production-grade PyTorch code you've written and be prepared to explain your thought process behind deploying ML models into real systems.
✨Understand the Company’s Mission
Familiarise yourself with Applied Computing's mission of sustainable abundance and how Orbital aims to optimise energy operations. This will help you connect your answers to their goals and show that you're genuinely interested in contributing to their vision.
✨Prepare for Real-World Scenarios
Expect questions about handling noisy sensors, partial observability, and uncertainty quantification. Think of examples from your past experience where you tackled similar challenges, and be ready to discuss how you would approach these issues in the context of Orbital’s foundational models.
