Machine Learning Researcher
Cambridge, England /
Please apply if you want to:
- Make a positive global impact on climate change and society
- Work in an interdisciplinary environment where you are always learning
- Develop approaches which have a direct impact on electricity grid operations
- Be part of a collaborative and sociable team
- Join an established Machine Learning lab in a growth phase
Invenia Labs’ research team work on real-world problems with a focus on understanding and improving the efficiency of electricity grids, to reduce greenhouse gas emissions and economic inefficiencies. Our researchers and developers work collaboratively to achieve our mission of optimising the world’s complex systems for the benefit of as many people as possible, bringing in expertise from a wide range of backgrounds. If this sounds like the environment for you, we are looking for Machine Learning Researchers to join our growing team.
- Design, prototype and improve the models used in our core system.
- Actively participate in research planning.
- Analyse relevant data, draw insights and communicate results to other team members.
- Collaborate on diverse projects with team members including Power Systems and Data Science Researchers, Research Software Engineers, and Developers.
- Communicate research and share knowledge internally and externally, such as in research papers and at conferences.
- You hold a PhD (or Master’s degree with industry research experience) in Machine Learning, Electrical Engineering, Mathematics, Physics, Computer Science, Statistics, or other related disciplines.
- You have expertise in at least two of these topics: Bayesian methods, probabilistic modelling, optimisation, or statistical inference on complex data.
- You have experience with numerical and scientific computing concepts and methods.
- You have good coding skills, preferably with experience in Python and/or Julia.
- You have hands-on experience with implementing models within machine learning frameworks (for example TensorFlow, JAX, Flux.jl).
It will also be beneficial (but not essential) if you have experience with:
- Decision-making under uncertainty
- Time-series forecasting
- Gaussian processes
Please note that while this is a flexible hybrid role, it cannot be performed fully remotely. We only consider candidates who already live, or are willing to relocate to our office locations in either Winnipeg, Canada or Cambridge, UK.