Machine Learning Researcher

Cambridge
Research
Full-time
Please apply if you are innovative and ambitious individual who wants to help build an organization that makes a positive global impact on society and the environment.

Invenia Labs is looking for Machine Learning Researchers to join a growing research and development team in Cambridge, UK. Our research team has a broad range of interests, including Machine Learning, Power Systems, and Complex Systems. From creating experiments and prototyping implementations to testing and deploying promising ideas quickly and safely, our researchers work on real-world problems with a focus on understanding and improving the efficiency of electricity grids. They are also active in the wider research community by partnering with universities, publishing research papers, and attending conferences. 

Role Description:

    • Design, implement, and test models to meet research goals.
    • Analyse relevant data and communicate results in the form of discussions and reports to other team members.
    • Develop well organised and documented code for individual projects or general use as necessary.
    • Collaborate with other researchers to meet research goals.
    • Present research clearly and concisely, both verbally and in writing, for internal and external use as appropriate.
    • Collaborate with and maintain relationships with external research labs, publish research papers and attend conferences.
    • Keep up with the literature in the academic domain.

Requirements:

    • MSc/PhD in Machine Learning, Electrical Engineering, Mathematics, Physics, Computer Science, Statistics, or other related disciplines.
    • Expertise with Bayesian methods, probabilistic modelling, optimisation, and statistical inference on complex data.
    • Experience with numerical and scientific computing concepts and methods.
    • Good coding skills, preferably experience with Python and/or Julia.
    • Experience with machine learning toolboxes and frameworks, such as TensorFlow and scikit-learn.