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Research Associate

University of Sheffield

Sheffield, United Kingdom

Sheffield University Management School, the Faculty of Social Sciences’ largest department, has achieved accreditation from the three largest and most influential business school associations: AACSB, AMBA and EQUIS.

The mission of Sheffield University Management School is to use our world class research base to develop knowledgeable and employable students, promote socially-responsible work practices and have a positive impact on organisations and society throughout the world. We sustain a research-intensive environment that creates, advances and disseminates knowledge with respect to management inquiry, for the benefit of students, private and public sector organisations and society in general. Our relocation to a fully refurbished building in June 2013 provides modern facilities in which to achieve our ambitions.

To start this research programme, we are looking to appoint a Research Associate in Sustainable Supply Chain in phase 1 of The Leverhulme Centre for Climate Change Mitigation (LC3M),Theme 4 (Social Science). You will be responsible for leading the development of code/area of life cycle analyses. Experience in quantitative data collection and analysis, especially in developing mathematical indicators and large scale datasets and database analytics is essential. You will hold a good first degree and a PhD in a relevant area (or have equivalent experience).

You will have an excellent record of high quality publications, as appropriate for your career stage. You will be expected and encouraged to develop your research profile through joint research activities, providing you with an excellent opportunity to develop your academic career potential.

You can view the supporting documentation by clicking on About the Job and About the University located near the top of your screen.

Main Duties and Responsibilities
• Modelling will be undertaken by extending the Supply Chain Environmental Analysis Tool, SCEnAT, developed in Sheffield, which integrates Traditional/Process LCA with Environmental Input-Output LCA. (Training will be provided to those who already have LCA experience)
• Mapping the EW supply chain (referring to Moosdorf prior work on EW energy costs will be our starting point).
• Identifying boundary.
• Gathering primary and secondary data.
• Computing and modelling in SCEnAT using hybrid traditional/process LCA and environmental input-output LCA.
• Designing scenario and modelling scenario on EW supply chain.
• Rethinking and theorising EW supply chain in resource flow context, and scaling up rock flow with other flows, e.g. water, nutrient, etc.
• Developing new comprehensive and integrated mathematical indicators and parameters for sustainability for measuring atmospheric, environmental, social, economic, ethics, risks and supply chain scales impact.
• Methodological advancement of hybrid LCA and SCEnAT to integrate with analyses in Themes 1, 2 and 3 (plus other social science analyses within Theme 4) to feed into a multi scale big data cloud based environment for predictive analysis, sustainability performance monitoring and decision making.
• Defining requirement of machine learning and cloud infrastructure to enable large scale data storage, processing and advanced analytics, dynamic modelling; and implement them.
• Undertaking high-quality research in line with project demands.
• Writing academic papers and reports in line with the Centres’ demands.
• Undertaking extensive literature research on science, social science and engineering based journals.
• Bids writing and proposals writing in line with the Centres’ demands.
• Presenting in conferences and meetings about the research work.
• Communicating with cross disciplinary research teams, policy makers, NGO, scientists, social scientists and stakeholders in this research.
• Understanding, analysing and interpreting advanced EW research in Themes 1-3, and integrating with the work in Theme 4.
• Supervising PhD students and early career researchers in this field.
• Any other duties, commensurate with the grade of the post.


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