Postdoctoral Position (f/m/d) | Integrated Assessment Modeling (Climate–Air Pollution–Economy) Full-time

Postdoctoral Position (f/m/d) | Integrated Assessment Modeling (Climate–Air Pollution–Economy)

Open postdoc position is available at the Max Planck Institute for Chemistry (MPIC), co-supervised by Prof. Yafang Cheng (Director at MPIC) and Dr. Wenjun Meng (Project Team Leader at MPIC). The project aims at developing an advanced integrated assessment modeling framework that links socio-economic, environmental, and technological factors to understand the impacts of climate and energy policies.

The Integrated Assessment Modeling team at the Aerosol Chemistry Department of the Max Planck Institute for Chemistry (MPIC) develops and applies modeling frameworks that connect economic systems, energy transitions, climate impacts, and air pollution to support policy-relevant analysis. The group integrates insights from climate science, atmospheric/aerosol research, and economic modeling, with interests including improved spatial resolution and machine-learning-enabled approaches for policy analysis.

Postdoctoral Position (f/m/d) – Integrated Assessment Modeling (Climate–Air Pollution–Economy)

Your Role

We invite applications for a Postdoctoral Researcher (f/m/d) to contribute to model development and scenario analysis at the interface of economics and environmental science. We particularly encourage candidates with a strong economics background and interest in environmental and energy applications. The successful candidate will work on model development, scenario analysis, and interpretation of policy-relevant results in close collaboration with an interdisciplinary team.

Key Responsibilities

• Contribute to the development and evaluation of integrated assessment modeling tools linking socio-economic pathways with environmental and climate outcomes.
• Design and run scenario experiments; analyze and interpret model results for policy-relevant questions.
• Document workflows and support reproducible research (model evaluation/validation, transparent assumptions).

Your Profile

• PhD in economics, environmental science or a closely related quantitative field (e.g., climate/energy policy, or other related field with strong quantitative/economic focus).
• Demonstrated interest in topics such as environmental economics, energy transitions, climate impacts, and air pollution.
• Strong quantitative skills (e.g., econometrics, optimization, I/O table, CGE, simulation modeling) and solid programming experience (e.g., Python/R/Matlab/C).
• Preferred (optional): familiarity with machine learning/statistical learning (e.g., regression/ML pipelines, neural networks, model evaluation), and experience using common ML libraries.
• Ability to work independently and communicate effectively in English in an interdisciplinary environment.

What We Offer

• Competitive compensation and benefits according to the applicable collective agreement (TVöD/MPG)
• Initial 1-year contract, extendable up to 2–3 years upon mutual agreement.
• An international, collaborative research environment at the Max Planck Institute for Chemistry.
• Access to strong scientific networks across atmospheric chemistry, climate science, and policy-relevant modeling.
• Support for professional development, publication, and conference participation.

How to Apply

Please email the following as a single PDF:

1. CV (including publication list)
2. Short statement of interest (~1 page: motivation, background and strength)
3. Contact information for at least two references (name, affiliation, email)

to Dr. Wenjun Meng at w.meng@mpic.de.

Optional Materials

• You may include additional materials that demonstrate your skills and capabilities, such as code repositories or project demonstrations.
• Reference letters can also be submitted directly at the time of application, though this is not required.

Applications will be reviewed on a rolling basis until the position is filled. The starting date is negotiable, but preferably no later than Autumn 2026.

About the Research Team

Prof. Yafang Cheng is the Director at the Max Planck Institute for Chemistry and head of the Aerosol Chemistry Department. She is internationally recognized for pioneering research on atmospheric aerosols and their impacts on air quality, human health, and climate. Her department combines field campaigns, instrument development, multiscale modeling, and machine learning, providing an exceptionally stimulating environment for innovative and interdisciplinary research. Prof. Yafang Cheng is a globally recognized leader in atmospheric and environmental sciences, with over 180 peer-reviewed publications (including multiple articles in Science, Nature Geoscience, PNAS). She is a Member of the Academia Europaea, Fellow of the American Geophysical Union (AGU), and Fellow of the American Association for the Advancement of Science (AAAS). As a supervisor, she is known for her inspiring mentorship, international perspective, and proven record of guiding students and postdocs toward impactful careers. Many of the group members have stepped into faculty positions at top universities.

Dr. Wenjun Meng leads the Integrated Assessment Modeling team at the Max Planck Institute for Chemistry, focusing on the co-benefits and trade-offs between air pollution mitigation and climate impacts. Her research integrates energy, economy, environment, climate, and public health to quantify how policy interventions shape sustainable development pathways. Dr. Meng received her PhD in Environmental Geography (with distinction) from Peking University and was a visiting scholar at Georgia Tech, IIASA, and the University of Valladolid. Her work has been published in journals such as PNAS and Environmental Science & Technology, and has advanced integrated analyses of residential energy use, air quality, and associated health and climate impacts. This includes evidence on the costs and benefits of residential emission mitigation, particularly for improving indoor air quality. Her work has received more than 3,000 citations with an h-index of 27 (Google Scholar).