Machine Learning Scientist Madingley, England
Job description
Who we are
British Antarctic Survey (BAS) delivers and enables world-leading interdisciplinary research in the Polar Regions. Our skilled science and support staff based in Cambridge, Antarctica and the Arctic, work together to deliver research that uses the Polar Regions to advance our understanding of Earth as a sustainable planet. Through our extensive logistic capability and know how BAS facilitates access for the British and international science community to the UK polar research operation. Numerous national and international collaborations, combined with an excellent infrastructure help sustain a world leading position for the UK in Antarctic affairs. British Antarctic Survey is a component of the Natural Environment Research Council (NERC). NERC is part of UK Research and Innovation www.ukri.org
We employ experts from many different professions to carry out our Science as well as keep the keep the lights on, feed the research and support teams and keep everyone safe! If you are looking for an opportunity to work with amazing people in one of the most unique places in the world, then British Antarctic Survey could be for you. We aim to attract the best people for those jobs
Purpose:
The British Antarctic Survey’s Artificial Intelligence Lab (www.bas.ac.uk/ai) is looking to hire a machine learning engineer/researcher. Initial focus will be to develop and deploy ML and computer vision methods to classify species from Antarctic seafloor photographic imagery. You will develop a working tool for automatic identification and quantification of Antarctic sea floor animals and environments, focusing on object detection, recognition, count, size, abundance quantification and object associations. This will enable fast biological data acquisition from images collected on science cruises and data available on the internet. The successful candidate will join the AI Lab’s ongoing activities, working as part of a team, to create generalisable tools and digital frameworks to underpin BAS science. The successful candidate will have the opportunity to work in close collaboration with our partner organisations , including: The Alan Turing Institute; international polar research institutes; our University network; and our Centres for Doctoral Training (CDTs) in AI for Environmental Risk. Candidates will have experience working in machine learning and/or deep learning.
Duties:
- To deploy machine learning methods to identify and quantify sea floor communities to help improve our scientific understanding, and support our Page 2 of 2 planning and/or monitoring activities
- To conduct creative and innovative research in AI for environmental science, and to develop significant outcomes through publications
- To work collaboratively with researchers and senior investigators from across BAS, and external partners
- To champion reproducible science and open-source infrastructure to empower the global environmental research community
- To advise masters-level, and PhD students on machine learning methods
- To represent BAS to key stakeholders, such as funding agencies and Government
- To disseminate research to both academic and non-academic audiences (including public engagement), contribute to the external visibility of BAS.
- To play an active role in advancing the BAS AI Lab
- To help create a friendly and approachable community of environmentally focused machine learning experts and facilitate integration with the BAS science and engineering teams
- Undertake other duties as appropriate as requested by the BAS Director
Job Types: Full-time, Fixed term contract
Contract length: 36 months
Salary: £31,931.00-£39,915.00 per year
Benefits:
- Casual dress
- Company events
- Company pension
- Cycle to work scheme
- Employee discount
- Flexitime
- Free parking
- On-site parking
- Referral programme
- Store discount
- Work from home
Schedule:
- Monday to Friday
Ability to commute/relocate:
- Madingley, Cambridgeshire: reliably commute or plan to relocate before starting work (required)
Education:
- Master's (preferred)
Work Location: One location
Reference ID: BAs 23/27
