Researcher in Environmental Model Emulation
Researcher in Environmental Model Emulation
- Job reference:
- BAS 21/139
- Contract type:
- Full Time
- Fixed Term - 3-months in the first instance
- Salary from £39,748 to £50,790 per annum.
- We offer generous benefits
- British Antarctic Survey
- BAS Cambridge
- Closing date:
- 31 October, 2021 11:59 pm
The British Antarctic Survey’s Artificial Intelligence Lab (www.bas.ac.uk/ai) is looking to hire a researcher to develop and apply emulation software for environmental simulators to tackle some specific science challenges selected from the interdisciplinary environmental activities of the British Antarctic Survey (https://www.bas.ac.uk/science/our-research/). In addition, the successful candidates will join the AI Lab’s ongoing activities, working as part of a team, to create a framework to underpin AI-based and physics-informed Digital Twins of the natural environment. The successful candidate will work in close collaboration with our partner organisations, including: The Alan Turing Institute; international research institutes; our University network; and our two Centres for Doctoral Training (CDTs) in Earth Observation and AI for Environmental Risk. Candidates will have experience working in machine learning and/or data science.
This post is being advertised for 3-months.
The post holder will develop and apply emulation software for environmental simulators to tackle some specific science challenges selected from the interdisciplinary environmental activities of the British Antarctic Survey (https://www.bas.ac.uk/science/our-research/). In addition, the successful candidates will join the AI Lab’s ongoing activities, working as part of a team, to create a framework to underpin AI-based and physics-informed Digital Twins of the natural environment. The successful candidate will work in close collaboration with our partner organisations, including: The Alan Turing Institute; international research institutes; our University network; and our two Centres for Doctoral Training (CDTs) in Earth Observation and AI for Environmental Risk. Candidates will have experience working in machine learning and/or data science.
A PhD or equivalent experience in a relevant field
- To develop machine learning and/or model emulation methods for environmental prediction, scientific understanding, planning and/or monitoring;
- To conduct outstanding, 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 the BAS AI Lab;
- Seek ways to develop societal impact;
- 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;
Some delivery of training activities may be required to support the development of AI methods across BAS research disciplines;
- Undertake other duties as appropriate as requested by the BAS Director.
The above list is not exhaustive, and the job holder is required to undertake such duties as may reasonably be requested within the scope of the post. All employees are required to act professionally, co-operatively and flexibly in line with the requirements of the post and UKRI
Please quote reference for any queries: BAS 21/139
Publication date: 08 October 2021
Closing date for receipt of application forms is: 31 October 2021
Interviews are scheduled to be held on: 17 November 2021
At BAS, our vision is to be a world-leading centre for polar science and operations. Making our vision a reality depends on the excellence and diversity of our staff. We are committed to creating a workplace where all our staff can flourish and succeed. BAS is a Disability Confident employer, we are proud to hold a bronze Athena Swan award and we are a member of enei, the Employers Network for Equality & Inclusion.
We appreciate the importance of achieving work-life balance and support this with a number of family and carer-friendly policies. Plus a flexible working policy for those who may wish to amend their working pattern or arrangement.
Skills are listed as either Essential or Desirable. Desirable skills importance rating in parenthesis (1 is high, 5 is low)
- Track record of engaging with partners from different domains of expertise; - Essential
- Excellent written and verbal communication skills, including experience in the visual representation of quantitative data, the authoring of research papers or technical reports, and giving presentations or training on technical subjects; - Essential
- Ability to communicate more complex, specialist, or conceptual information clearly and persuasively. - Essential
- Effective communicator and networker within and outside the research community (creating networks); - Desirable 
Computer / IT skills
- Excellent computer skills - Essential
- Proficiency in one or more modern statistical programming languages used in research in data science and machine learning, such as Python or Julia - Essential
- Experience of working with large datasets and writing scalable code - Essential
- Experience in computational statistics, particularly Bayesian modelling and Bayesian statistics - Desirable 
- Contribution and engagement with open-source software communities - Desirable 
- Record of successful decisions made working collaboratively - Essential
- Commitment to professional development of their own career; - Essential
- Willingness to act as mentor for others; - Essential
- Ability to interpret and share knowledge by advising and guiding others as required. - Essential
- Ability to foster an innovative and creative environment for research - Desirable 
- Proven record of ongoing development of their technical skills and knowledge; - Essential
- Flexible attitude towards work. - Essential
- An understanding of the importance of good practice for producing reliable software and reproducible research (e.g. version control, Jupyter notebooks); - Desirable 
- An interest in methodological advances in environmental sciences - Desirable 
- A PhD or equivalent experience in a relevant field - Essential
Resource Management ability
- Experience in managing, structuring, and analysing research data - Essential
- Ability to lead one’s own work independently, and collaborate productively as part of a team, in order to meet milestones and deadlines. - Essential
Skills / Experience
- Expertise in state-of-the-art methods in data science and computational intelligence; - Essential
- Ability to lead on developing and writing papers in the application of machine learning; - Essential
- Ability to rapidly assimilate new ideas and techniques on the job and apply them successfully; - Essential
- Ability to work and interact professionally within a team of researchers and students. - Essential
- Track-record on leading papers - Desirable 
In order to apply, please complete and upload all the application forms listed below. If you experience any issues in uploading your Application Form, please email us your complete application, making reference to the job reference and title.
Apply by post:
Due to the COVID-19 outbreak, the Recruitment team is working remotely and cannot access the building. If you would like to apply for this job but cannot apply online, please contact us: [email protected]
If you need more information
- [email protected]
- +44 (0)1223 221400
- +44 (0)1223 362616
The information you provide during the application process will only be used for the purpose of progressing your application, to fulfil legal or regulatory requirements where necessary or, in the case of the Equal Opportunities Monitoring Questionnaire, to help BAS meet its equal opportunities policy.
The British Antarctic Survey will not share the information you provide with any third parties, and the information will be held securely by the British Antarctic Survey whether the information is in electronic or physical format. Please note that the Equal Opportunities Monitoring Questionnaire will be detached from your application prior to the short-listing of candidates for interview.
Unsuccessful applications will be securely destroyed 6 months after the end of the recruitment process (or 1 year after the end of the recruitment process in the case of Marine Staff and AEP. The applications of successful applicants will be retained as part of their personnel file.
Further information can be found in the information notice of NERC, our parent body.