PhD Candidate in Prospective LCA — Remote & Flexible

CNRS; France; Hybrid; EUR 20,000 - 26,000; Full time

Benefits offered by this job

RTT 44 days

Remote work options

Public transport subsidy 75% + up to €

Job summary

CNRS is seeking a PhD candidate to join the UTOPII unit and work on assessing environmental impacts of engineering research through prospective Life Cycle Assessment (pLCA) and EEIO techniques. You will collaborate with researchers to develop and test a methodological framework within the ADEME PROgSPECT project.

The project emphasizes interaction with teams, dissemination of results, and exploration of future scenarios such as Business as Usual and Sufficiency.

Qualifications

• Master's or engineering degree in engineering sciences.
• Experience conducting Life Cycle Assessment (LCA) in engineering sciences.
• Desirable: experience in prospective LCA and related tools such as PREMISE or EEIO methods.

Responsibilities

• Couple environmental assessment methods with pLCA (EEIO, etc.).
• Synthesize literature on pLCA for research projects and programs.
• Develop guidelines for defining and modeling prospective scenarios.
• Analyze and test available and emerging pLCA tools.
• Contribute to a formal methodological framework for prospective environmental assessment of research programs in engineering sciences.
• Apply to an emerging technology in the field of engineering sciences (materials, nanotechnologies, electronics, etc.).
• Disseminate results in conferences and journals.

Skills

LCA experience

Programming (Python)

Communication skills

Education

Master's or engineering degree in engineering sciences

Tools

Python

GitHub

Job description

CNRS is seeking a PhD candidate to join the UTOPII unit and work on assessing environmental impacts of engineering research through prospective Life Cycle Assessment (pLCA) and EEIO techniques. You will collaborate with researchers to develop and test a methodological framework within the ADEME PROgSPECT project.

The project emphasizes interaction with teams, dissemination of results, and exploration of future scenarios such as Business as Usual and Sufficiency.