Machine Learning Engineer/Researcher – Environmental Science (Band E)

Job reference:
BAS 21/48
Contract type:
Full Time
Duration:
Fixed-Term (3 years)
Salary:
£38,969 - £49,794 per annum
Benefits:
We offer generous benefits
Team:
British Antarctic Survey
Location:
Cambridge
Closing date:
11 April, 2021 11:59 pm

Due to the COVID-19 outbreak, please note there may be delays to our recruitment timeline.

Description

The British Antarctic Survey’s Artificial Intelligence Lab is looking to hire three machine learning engineers/researchers. Initial focus will be to develop and deploy ML and computer vision methods 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 three successful candidates 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. Areas which could be developed include:

  • Computer vision, e.g., for monitoring wildlife within satellite imagery
  • Deep learning and interpretability methods for improving scientific understanding
  • Probabilistic time series modelling for forecasting environmental change
  • Uncertainty quantification through Bayesian statistical methods

We are advertising a similar role at a different level of experience: Machine Learning Engineer/Researcher to be appointed to UKRI Salary Band D If you are interested, please visit the page. The interview process for both positions will be combined on the same week.

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

To develop and deploy ML and computer vision methods to tackle some specific science challenges selected from the interdisciplinary environmental activities of the British Antarctic Survey.

Qualification

A PhD or equivalent experience in a relevant field

Duties

  • To deploy machine learning 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: BAS 21/48
Publication date: 11 February 2021
Closing date for receipt of application forms is: 11 April 2021
Interviews are scheduled to be held: End of April / Early May

BAS is an Equal Opportunity employer. As part of our commitment to equality, diversity and inclusion and promoting equality in careers in science, we hold an Athena SWAN Bronze Award and have an active Equality, Diversity and Inclusion programme of activity. We welcome applications from all sections of the community. People from ethnic minorities and disabled people are currently under-represented and their applications are particularly welcome. We operate a guaranteed interview scheme for disabled candidates who meet the minimum criteria for the job and will provide necessary adaptations for the interview. We are open to a range of flexible working options, including job sharing, to support childcare and other caring responsibilities.

Skills are listed as either Essential or Desirable. Desirable skills importance rating in parenthesis (1 is high, 5 is low)

Communication skills

  • Track record of engaging with partners from different domains of expertise - Essential
  • Effective communicator and networker within and outside the research community (creating networks); - Desirable [2]
  • 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

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 [4]
  • Contribution and engagement with open-source software communities - Desirable [3]

Decision Making

  • Ability to communicate more complex, specialist, or conceptual information clearly and persuasively - Essential

Interpersonal skills

  • 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 [2]

Numerical ability

  • Significant experience in developing and applying predictive models - Essential

Other Factors

  • 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 [2]
  • An interest in methodological advances in environmental sciences - Desirable [3]

Qualifications

  • 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 [1]

You can apply for this job online or you can print off the application forms, fill them out by hand and mail them.

Apply Online:


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
[email protected]
Telephone:
+44 (0)1223 221400
Facsimile:
+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.