An opportunity has arisen for an outstanding Ph.D. student to join the industrial ecology team at the University of Edinburgh to work on Foundational Models for Sustainable Development: A Unified Systems Analysis Approach.
Modern society depends upon the use of many materials. Sustainable development requires these materials to be effectively managed with respect to a desired carrying capacity of the Earth, in order to meet the needs of the present without compromising the needs of the future. Such a goal can be achieved if modern society’s use of materials (and energy) is managed using a systems perspective which views them in a holistic context that includes both anthropogenic (e.g., industrial) and natural systems.
Stocks and flows data provide the fundamental information that enable materials and energy use throughout society to be understood and managed. This information may describe supply chains of companies, enable environmental impacts of products and/or services to be quantified, and facilitate understanding of future resource availability. Therefore, these data are becoming increasingly more important. However, they are also becoming increasingly more complex as industrial systems become larger and more globalised, populations grow, and as urban environments become more developed.
Furthermore, stocks and flows data currently exist mainly as discrete packages, with highly varied type, scope, and structure. These factors constitute a powerful barrier to their holistic integration, and thus also to efficient harnessing of current and yet to be published stocks and flows data. A unified data structure that can integrate all of these seemingly disparate and dispersed stocks and flows data, and unified systems analysis models that can harness them, would facilitate a paradigm-shift in our understanding of materials and energy use throughout society. These models would provide a significantly enhanced foundation for approaching sustainable development.
Leveraging the unique and timely opportunity that is now available from recent work of Dr. Rupert J. Myers (https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636509) that synthesised nearly two decades of material stocks and flows data – the most comprehensive diverse collection of such data that exists anywhere – the Ph.D. student leading this project will develop a comprehensive and integrated model of stocks and flows throughout the industrial system. The model will seek to optimise the total materials cycle using the principles of mass and energy conservation. The total materials cycle and its data may notably be represented in matrices, tree type hierarchies, and Sankey diagrams.
Elements and engineering materials included in the database and model will range from aluminium to ytterbium, and steel to ammonium paratungstate. It will contain complete material cycles, including key stages such as production of engineering materials, fabrication and manufacturing, use, waste management, and the environment. It will use historical and contemporary data. Data gaps will be filled by estimation, e.g., using a probabilistic approach, to enable complete integration of material cycles in the unified model/data structure.
It is anticipated that application of the model will provide new insights into material efficiency, recycling, criticality, and substitution across the periodic table, particularly of composite engineering materials for which these properties are presently relatively poorly characterised. Project outputs will facilitate new understanding around technology choices that society should take to approach sustainable development; therefore, they will be relevant to policymakers, industry, and academia.
The model may be developed in Python and link to a database of material and energy stocks and flows data in CSV, MySQL, or PostgreSQL formats. Ideally, the model and database would also link to a user friendly web interface, so that it is also accessible to non-experts and researchers that are not proficient in coding. Therefore, Ph.D. candidates with good computational and/or coding skills, a background in physical/natural sciences and engineering, and an interest in sustainability are highly encouraged to apply.
Flexible – applications will be considered on a rolling and individual basis.
20th December 2018 (or until vacancy is filled). Interested applicants are advised to submit their application as soon as possible, as applicants will be reviewed and interviews will be conducted up until the position is filled.
Applicants must hold an undergraduate or masters degree in one or more of the following areas: engineering, economics, science (informatics, physics, chemistry, materials, environmental science, forestry, etc.). Experience in statistical data analysis and informatics (e.g., coding in R, Python, etc., and database management) is highly desirable but not essential, although in the latter case a self-motivation to learn such skills is necessary.
Applicants must be enthusiastic and driven to learn and work across traditional discipline boundaries.
Further information on English language requirements for EU/Overseas applicants.
Tuition fees and stipend are available for Home/EU students (International students can apply, but the funding only covers the Home/EU fee rate).