Job title: Senior Research Scientist - AI/Optimization for Energy Systems

About H2GO Power

H2GO Power is an award winning startup specializing in safe, efficient and smart hydrogen-energy storage solutions for a net-zero emissions future. Our fast-growing team is dedicated to solving sophisticated technical problems with the goal to provide clean and reliable power from renewable sources and help counter climate change with technology.

Job Summary

‘HyAI’, our software product, is used to optimize the design and real-time control of renewable-powered hydrogen systems. We are looking for a talented researcher, with expertise in mathematical optimization and machine learning, to further improve the models used by HyAI and apply these models to a variety of real-world use cases.

Benefits

• 25 days of annual leave (excluding bank holidays, and growing with years of service)
• Health Cash Plan
• Annual salary reviews
• Annual allowance of £200 for self-development
• Summer and Winter Town Halls/Parties
• Monthly Company Lunch and Learn Sessions
• Vibrant and supportive Company Culture fostering growth
• EMI scheme based on performance agreed after 12 months of employment
• Company Pension Scheme (8% total)
• Weekly Yoga and Fitness classes are offered at Scale Space
• Weekly Socials, seasonal celebrations, frequent community parties and educational events at the Scale Space facility

Roles and responsibilities

• Design, implement and evaluate mathematical optimization and machine learning models for energy systems applications
• Write clear, well-documented and efficient code
• Analyze and manipulate real-world datasets
• Stay up-to-date with the relevant developments in the fields of mathematical optimization and machine learning
• Work collaboratively as part of a cross-functional team in an agile development environment
• Mentor and train team members to accelerate their professional development and enhance team productivity

Qualifications and skills

• Masters or PhD in Operations Research, Computer Science, or a related field
• 3+ years of experience in a technical (a must), research-focused role within the energy industry (preferably)
• Expertise in the majority of the following mathematical optimization topics:
• Linear and mixed-integer programming
• Optimization under uncertainty (e.g. stochastic programming, robust optimization)
• Gradient-free optimization methods (e.g. bayesian optimization)
• Practical experience implementing supervised learning models, in particular time series forecasting
• Strong programming abilities in Python, including familiarity with:
• Functional and object-oriented programming paradigms
• Optimization modeling packages for interfacing with commercial (e.g. GUROBI) and open-source (e.g. CBC, HiGHS) solvers, such as gurobipy, mip, cvxpy or pulp
• The Python data science ecosystem, including packages such as numpy, scipy, pandas, and sklearn
• Data visualization, using a package such as plotly or matplotlib
• Jupyter notebooks
• Version control using Git and GitHub
• Bonus: hydrogen-specific domain knowledge, familiarity with physics-based modeling using ODEs and PDEs