Research Associate, Machine Learning for Sea Ice Prediction – Alan Turing Institute

Company Description

The Alan Turing Institute is the UK’s national institute for data science and artificial intelligence. The Institute is named in honour of the scientist Alan Turing and its mission is to make great leaps in data science and artificial intelligence research in order to change the world for the better.

Position

The Data Science for Science and Humantities programme works alongside researchers from all disciplines across the Turing’s university partner network, and with national research facilities, to make effective use of state of the art methods in artificial intelligence and data science.

The programme is looking to recruit a Research Associate (RA) to develop new machine learning/deep learning/computer vision methods to monitor and predict sea ice change around Antarctica using synthetic-aperture radar (SAR) imagery and other observational data from satellite and surface sensors, as part of an international collaboration. More specifically, the RA will work as part of a cross-institute team to build upon our seasonal sea ice forecasting framework, IceNet, by integrating additional (including SAR) data.

The candidate will work within Turing’s Environment and Sustainability research theme, which seeks to develop methods to provide the meaningful insight to inform decision-making, improve risk management and enhance our resilience to climate change will require working across disciplines, bringing together methodology and expertise from different fields to develop tools and computational frameworks that can integrate data from multiple sources, available at different spatial and temporal resolutions and with different biases and uncertainties.

ROLE PURPOSE

We are seeking a Research Associate to join a newly funded research programme to explore the environmental drivers of Antarctic sea ice as part of a national and international collaboration.

In addition, the RA will have the opportunity to contribute to an open-source toolkit for scientific image analysis, in collaboration with other members of the wider Turing community. These general approaches will enable applications across a range of scientific research domains, such as optical microscopy and the monitoring of wildlife from remote sensing data.

The RA will play an active part in all aspects of research from data preparation, to the development of research questions, modelling and analysis, and writing up/publication. Technical meetings will take place between the partner institutions, establishing a robust platform for developing future programmes between environmental science researchers, The Alan Turing Institute and the wider scientific community. This is a collaborative research role and so it is crucial that you enjoy working with others, and are responsive within an interdisciplinary research environment.

We are looking for experience in one or more of the following areas: deep learning; computer vision; statistical machine learning; modern statistical programming languages including probabilistic programming; applying the principles of reproducible data science.

Applications are welcome from a wide range of disciplinary backgrounds, including the physical sciences, computer science, statistics, or mathematics, and particularly from candidates whose prior research has a strong computational focus.

The postholder will be line managed by Dr Scott Hosking, Senior Research Fellow, and will work closely with other environmental researchers at the Turing, as well as collaborators at the British Antarctic Survey (BAS) and University of Leeds.

DUTIES AND AREAS OF RESPONSIBILITY

  • Attend and present research updates at regular meetings, and contribute to the external visibility of the Institute.
  • Write or contribute to publications or disseminate research findings using other appropriate media
  • Adhere to modern principles of reproducible data science in carrying out their responsibilities.
  • Drive collaboration with academic experts and broader research partners from across the Turing and the wider Turing / project community.
  • Help create a friendly and approachable community of environmentally-focused experts, datasets and engineers, and facilitate integration with Turing’s research programmes;
  • Contribute to the broader research aims and challenges of the Turing Data Science for Science and Humanities programme, and ensure positive feedback to the project partnership.
  • Contribute to the life of the Institute and support its community

Please see the Job description for a full breakdown of the duties and responsibilities as well as the person specification.

Requirements

Essential

  • A PhD (or equivalent experience and/or qualifications) in a relevant area which will include the Physical Sciences, Mathematics, Statistics, Computer Science, or related discipline
  • Demonstrable experience or interest in statistical modelling; statistical machine learning; modern statistical programming languages including probabilistic programming; or applying the principles of reproducible data science.
  • Experience in performing data analysis on substantial real-world problems
  • Significant experience of using a modern statistical programming language (e.g. Python)
  • Excellent written and verbal communication skills, including experience in the visual representation of quantitative data, documentation of software packages or data resources, the authoring of research papers or technical reports, and giving presentations or classes on technical subjects.
  • Ability to plan and implement rigorous analysis plans.
  • Network with others with shared interests, collaborating on projects and strengthening future relations.

Other information

APPLICATION PROCEDURE

If you are interested in this opportunity, please click the apply button below. You will need to register on the applicant portal and complete the application form including your CV and covering letter. If you have questions about the role or would like to apply using a different format, please contact us 020 3862 3546, or email recruitment@turing.ac.uk.

CLOSING DATE FOR APPLICATIONS: 24 April 2022 at 23:59 

TERMS AND CONDITIONS

This full time post is offered on a 2-year fixed term basis. The annual salary is £37,000 – £44,000 plus excellent benefits, including flexible working and family friendly policies, https://www.turing.ac.uk/work-turing/why-work-turing/employee-benefits

Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant with a salary range of £34,510 per annum

EQUALITY, DIVERSITY AND INCLUSION

The Alan Turing Institute is committed to creating an environment where diversity is valued and everyone is treated fairly. In accordance with the Equality Act, we welcome applications from anyone who meets the specific criteria of the post regardless of age, disability, ethnicity, gender reassignment, marital or civil partnership status, pregnancy and maternity, religion or belief, sex and sexual orientation.

We are committed to building a diverse community and would like our leadership teal to reflect this. We therefore welcome applications from the broadest spectrum of backgrounds.

Reasonable adjustments to the interview process will be made for any candidates with a disability.


Please note all offers of employment are subject to obtaining and retaining the right to work in the UK and satisfactory pre-employment security screening which includes a DBS Check.

Full details on the pre-employment screening process can be requested from HR@turing.ac.uk.

This role is located at The Alan Turing Institute, 96 Euston Road, London, NW1 2DB.

From 01 March 2022 we will trial a Hybrid Working Model for an initial six-month trial period. During this period, staff will be expected to work at our British Library office for a number of days per month, dependent on the requirements of the role. As a guide, we anticipate this will be between 2-4 days per month, but the hiring manager will be able to confirm this during the interview.

Leave a Reply

Your email address will not be published. Required fields are marked *

*

This site uses Akismet to reduce spam. Learn how your comment data is processed.