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Research Associate*/Research Assistant in Sustainable Research Practices (Computational Science) (Fixed Term)

University of Cambridge - Department of Public Health and Primary Care, Cardiovascular Epidemiology Unit

Location: Cambridge
Salary: £32,296 to £45,163
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 8th November 2024
Closes: 25th November 2024
Job Ref: RH43995

An exciting opportunity has arisen for a new research role as part of the Green Algorithms Initiative in the Department of Public Health and Primary Care, one of Europe's leading academic departments of population health sciences. The post will suit researchers interested in understanding how computational scientists can be incentivised to conduct research more sustainably.

You will contribute to the design, rollout and data analysis of an international multi-centre trial studying the effectiveness of carbon reporting tools in reducing the environmental impacts of research computing. This will involve designing and refining study methodology, ensuring proper ethics approval is obtained, coordinating with the different centres for data collection, and analysing and interpreting the resulting data.

This research will provide internationally unique and unusually detailed insights into the effectiveness of carbon monitoring tools at reducing computing carbon footprint. You will be trained to obtain the required expertise in green computing and environmental sustainability, and will be able to draw on the world-leading methodological expertise in study design of PHPC, the Cardiovascular Epidemiology Unit and the THIS Institute. Your work is expected to lead to first author high-impact publications.

The Green Algorithms Initiative (http://www.green-algorithms.org/), led by Dr Loïc Lannelongue, is a world-leading project in the field of green computing focusing on quantifying and reducing the environmental impacts of computational science through open-source resources. The tools and frameworks developed and maintained by the group are used internationally and include the popular Green Algorithms online calculator, server-specific monitoring tools and the GREENER Principles for Environmentally Sustainable Computational Science. The Green Algorithms Initiative has received both nominations and awards for its contributions to environmentally sustainable research (e.g. HDR-UK Impact Award).

You will work in close conjunction with the senior scientists in the Initiative, including Dr Loïc Lannelongue and Professor Michael Inouye. You will also work closely with other members of the Department and scientific collaborators based in other institutions.

The preferred candidates will have an MSc or PhD (or equivalent experience) in Statistics, Epidemiology, Health Data Science or other related subject. Applicants will have a strong understanding of study design and data analysis, ideally with an understanding of behavioural studies, and an interest in the intersection between computing, science and environmental sustainability.  They should have an ability to communicate and present results to other scientists along with excellent verbal and written communications skills and strong organisational skills.

*Appointment at Research Associate level is dependent on having a PhD (or equivalent experience), including those who have submitted but not yet received their PhD. Where a PhD has yet to be, awarded appointment will initially be made at research assistant and amended to research associate when the PhD is awarded (PhD needs to be awarded within 6 months of the start date). If an individual has not submitted a PhD or is not working towards one, they could be appointed as a Research Assistant if they have either a degree (and/or Master's) in a relevant area or equivalent experience.*

The funds for these posts are available for 2 years, in the first instance from commencement in post.

This is a full-time position we welcome applications of no less than 60% FTE.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

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