진학프로석·박사 채용 접수 플랫폼

BRGM is the French Geological Survey, the leading public institution for Earth Science applications for the management of surface and subsurface resources with a view to sustainable development.

BRGM is actively involved as a coordinator and as a partner in several EU and French projects supporting technologies for resources efficiency, mineral intelligence, circular economy policies and environmental impacts mitigation.

The green transition is (also) a materials transition.

It implies that the demand for a number of raw materials will significantly increase in the short-, mid- and long-terms.

In particular manufacturing of photovoltaic (PV) systems requires various materials such as silicon metal, copper, indium, gallium, selenium, cadmium, tellurium and germanium.
There is a necessity to bring transparent and reliable data and methods to address the actual sustainability (here considering its environmental dimension) of such large-scale changes.

Skills/Qualifications

In the last 20 years, Life Cycle Assessment (LCA) has been ever more widely implemented to assess the potential environmental impacts of the use of the subsurface in a cradle-to-gate perspective.

Moreover, several recent spatially-explicit studies quantified the share of mining activities in areas which face high levels of environmental constraints (water stress, land use).

Yet there is still the need to significantly improve both the methods and the data implemented in Life Cycle Thinking, for a consistent, complete and representative environmental assessment of the uses of the subsurface.

This post-doc position is funded under the frame of the LCA-TASE project, funded by France 2030.

LCA-TASE aims to provide French stakeholders in the energy transition with advanced LCA methodologies to have reliable metrics on the environmental, social and economic impacts of renewable energies, i.e. photovoltaic and wind turbines, in order to assess their sustainability from a life cycle perspective.

The candidate will extend the current knowledge on the environmental footprint of PV raw materials extraction and processing. The work will firstly build on a scrapping tool, already developed and tested at the BRGM, to massively extract environmental (and linked) data associated with raw materials production: energy use, water use, emissions to environment, processes, geology of the deposits, etc. Data will be extracted with focus on PV raw materials, considering environmental reports, scientific articles and other sources. The compiled datasets will be further:

i) processed, to derive up-to-date, more consistent and granular, Life Cycle Inventory datasets in a format suitable for the project

ii) and analyzed, in order to inform on environmental impacts induced by PV raw materials extraction and processing.

In the last part of this post-doc position, the candidate will address environmental risks to which raw materials supply chains are faced; i.e. making a bridge between the outcomes of this work on global and local environmental footprinting of PV raw materials (climate change, water use, land use, etc.), and criticality assessment.

The candidate will disseminate results of the activities to their team at BRGM, to the LCA-TASE project consortium, and to the scientific community through the writing of scientific articles and participation to conferences.

Starting date : 05/06/2026

Contract type : Post-doctoral contract – 2 years

Candidates must hold a PhD, in Industrial Ecology, Process Engineering or Mining Engineering, with demonstrated link/application to the mineral raw materials sector.

The candidates should demonstrate the following skills:

• - coding in Python
• - Life Cycle Thinking (LCA, MFA), or spatially-explicit environmental footprinting, applied to mineral raw materials
• - Knowledge/use of LCA software (e.g. Brightway) and LCI databases (e.g. ecoinvent)

Moreover, the following knowledge are assets for this position:

• - Mineral raw materials sector and criticality assessment methods
• - Data analysis, including statistical treatment of massive and heterogeneous data

The following competencies are required:

• - English (written, spoken, read) – French is an asset
• - Autonomous in day-to-day work
• - Integration in an interdisciplinary team
• - Communication + Scientific dissemination