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Job summary
The Grantham Institute – Climate Change and the Environment, was established at Imperial College London in 2007 to act as a central hub to pull together the different work taking place across the university that can help tackle climate change and environmental challenges. The innovation dimension of the Grantham Institute’s work is run under the banner Undaunted.
Undaunted’s mission is to nurture a climate innovation ecosystem that enables the creation of scalable innovative solutions to the climate challenge at pace. To achieve this mission, Undaunted is building a pipeline of routes into climate entrepreneurship, incubating innovators through our Greenhouse accelerator programme, delivering an ecosystem that empowers impact and creating a landscape where climate innovation can flourish.
We are now launching an exciting new experimental activity at the very start of the climate innovation pipeline: the Climate Solutions Catalyst. The objective of the programme is to test the hypothesis that the UK university sector has already made discoveries and inventions that can help tackle climate change, if only they can be found and supported properly. Working collaboratively with as many other UK universities as possible, and a full range of stakeholders, this two-year trial programme will try and unearth neglected Climate Solutions from the full breadth and depth of the UK research community, and then test potential support offers for these solutions and the researchers and innovators that discovered them.
This project has the potential to have significant impact in tackling climate change and improving the climate innovation pipeline in the UK and, eventually, more widely.
The search dimension of the project will use elements of data engineering, Machine Learning and AI. In addition to the impact objectives, the Research Fellow will have the opportunity to publish outcomes and consider broader applicability of the tools and methodologies developed in this work.
We are looking for a talented Machine LearningResearch Fellow interested in applying their skills to unlocking climate innovation. The post holder would work on scoping, developing, testing, and implementing data analysis and Machine Learning models and data-driven solutions to evaluate and improve the process of identifying, supporting, and scaling climate innovations within the UK academic sector. This Research Fellow will work closely with the other members of the Climate Solutions Catalyst and Undaunted to ensure that their work has as wide an impact as possible.
Duties and responsibilities
The Machine LearningResearch Fellow will have a critical role in designing and delivering the Machine Learning dimension of this project and integrating it into the work of others in the team who will be forming relationships across the UK and delivering human search dimensions. We would expect the postholder to maintain good working relationships with a range of key actors and be focused on using their research for practical outcomes.
As the only technical Machine Learning specialist in the team, we will expect the post holder to be able to explain their approaches in plain English to others and integrate user needs and other priorities effectively into their approaches.
You will be an active member of the Undaunted team, working with other members of the team to deliver on our wider mission to drive forward climate innovation in the UK and beyond.
Essential requirements
Our ideal candidate will have excellent technical skills with an understanding of how to apply these skills in a practical setting to deliver outcomes. You will have interest in applying your skills to the climate change problem space where innovations are needed, coupled with the natural curiosity required to support the delivery of this programme.
The successful candidate will have a combination of strong analytical and communication skills.
Further information
This role is full time, fixed term for 1 year.
Please note this role will be based at both South Kensington and White City Campus
Should you require any further details about the role please email: Gosia Gayer: g.gayer@imperial.ac.uk
For technical issues when applying online please email support.jobs@imperial.ac.uk
This is a hybrid role. Staff working in roles that are suitable for hybrid working will normally be expected to work 60% of their time onsite. The opportunity for hybrid working will be discussed at interview.
More information is available on the following web page: Work Location Categories (from 30 September 2023) | Administration and support services | Imperial College London
The College is a proud signatory to the San-Francisco Declaration on Research Assessment (DORA),which means that in hiring and promotion decisions, we evaluate applicants on the quality of their work, not the journal impact factor where it is published. For more information, see https://www.imperial.ac.uk/research-and-innovation/about-imperial-research/research-evaluation/
The College believes that the use of animals in research is vital to improve human and animal health and welfare. Animals may only be used in research programmes where their use is shown to be necessary for developing new treatments and making medical advances. Imperial is committed to ensuring that, in cases where this research is deemed essential, all animals in the College’s care are treated with full respect, and that all staff involved with this work show due consideration at every level.
http://www.imperial.ac.uk/research-and-innovation/about-imperial-research/research-integrity/animal-research
Documents
- Job Description - NAT01645..pdf
