An opportunity has arisen for an outstanding Ph.D. student to join the industrial ecology team at the University of Edinburgh to work on The Industrial Ecology Genome Project.
“The Human Genome Project (HGP) [was] an international research effort to determine the DNA sequence of the entire human genome.” (https://www.genome.gov/11511417/what-is-the-human-genome-project/)
This Ph.D. project will seek to answer the analogous question in the industrial system: how are material stocks and flows processed throughout the global physical economy?
A holistic understanding of how materials and energy are processed is fundamental to sustainability science and a core focus of industrial ecology. This information provides a basis upon which processing of materials and energy can be improved. Key challenges in this space range from quantifying and reducing environmental impacts of supply chains of products to understanding and adapting to future resource (un)availability.
Effectively managing the processing of materials and energy in order to address these challenges is becoming increasingly complex and more important as industrial systems become larger and more globalised, populations grow, and as urban environments become more developed.
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 will develop a comprehensive and integrated model of material and energy stocks and flows throughout the global physical economy. Elements and engineering materials included in it 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 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 not well characterised.
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 computational and/or coding backgrounds and an interest in sustainability are highly encouraged to apply.
Flexible – applications will be considered on a rolling and individual basis from the advertisement start date.
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.
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere