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Client Server in the United Kingdom is seeking a PhD-level Machine Learning Researcher to lead the scientific direction at the intersection of physics and AI. You will design and deploy production-ready models that transform geospatial data into trusted insights for environmental and defence-related applications.
Based near Cirencester, you’ll work in a hybrid setup with 2 days in the office and up to 3 days remote, shaping the research agenda from day one and mentoring others as the team scales.
Are you a PhD educated Machine Learning Research Scientist looking to apply your research to one of the world's most important environmental challenges?
You could be progressing your career at a well funded start-up developing an AI powered intelligence platform that transforms satellite imagery and scientific data into actionable insights about the Arctic. Combining cutting edge machine learning with physics based modelling, the team is tackling problems with real-world impact on climate, infrastructure and global decision-making (please note this is used for Defence clients as well as environmental).
As the first senior Machine Learning Research Scientist, you'll play a pivotal role in defining the company's scientific direction, leading the intersection of physics and AI.
Collaborating with the Science Lead, you'll design and deploy production ready machine learning models that transform complex geospatial and remote sensing data into trusted, explainable insights. Rather than relying on black-box AI, you'll develop physics informed models that are transparent, scientifically rigorous and validated against real world observations.
This is a rare opportunity to shape the research agenda from day one, influence future scientific hires and see your work deployed into production rather than remaining in academic papers.
You’ll be based in a beautiful Cotswolds (near Cirencester) office, with stunning views and a range of facilities including free parking onsite (not commutable on public transport) two days a week with flexibility to work from home the other three days.