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yanghuannn

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[交流] 德国马克思普朗克化学研究所,全奖博士/岗位制博士后:AI+计算化学

德国马克思普朗克化学研究所,全奖博士/岗位制博士后
方向一:AI+量化做有机反应网络自动生成
方向二:机器学习力场结合增强采样研究相变,凝结成核,及表界面过程
主要针对大气气溶胶系统,也非常欢迎非大气方向,但计算分析能力强的同学申请,相关大气背景可过来后补充

PhD / Postdoc Opportunity: AI-Driven Molecular Modeling for Organic Reactions and Multiphase/Interfacial Chemistry
Open PhD and Postdoc positions are available at the Max Planck Institute for Chemistry (MPIC), co-supervised by Prof. Yafang Cheng (Director at MPIC) and Dr. Huan Yang (Research Team Leader at MPIC). We develop AI-powered molecular modeling tools to uncover how molecules form atmospheric aerosols — tiny particles with huge impacts on climate, air quality, and health. Website: https://www.mpic.de/5587552/aerosol-chemistry

We are seeking PhD or postdoctoral fellows to work on either of the two directions:
1.        Autonomous reaction discovery: combining quantum chemistry, machine learning, and automation to chart oxidation pathways of atmospheric organic reactions.
2.        AI-augmented molecular simulations: developing and applying machine-learning force fields and state-of-the-art enhanced sampling to reveal interfacial and multiphase processes responsible for aerosol nucleation, growth, and interaction at the nanoscale.
Applicants without prior background in atmospheric or aerosol science are also encouraged to apply. We particularly welcome strong candidates from physics, engineering, computational science, or other areas of chemistry, as long as they demonstrate high motivation, solid theoretical or analytical skills, or strong coding ability.

Your Opportunity
We see aerosol chemistry as a unique and rapidly emerging frontier for computational and machine learning approaches. Historically, modeling efforts in this field were limited due to accuracy and system size, leaving many key questions unresolved. Now, with new AI-driven molecular modeling tools, there is enormous potential to make fundamental, high-impact discoveries. This creates an exceptional career pathway for young researchers who want to shape a new direction at the intersection of computational science, AI, and chemistry.
The methods we develop are intended to be general and transferable. The automated multiscale modeling and AI-augmented simulation frameworks will be broadly applicable to other domains where reaction kinetics, nucleation, and nanoscale processes are central — such as combustion science, retrosynthetic pathway discovery, energy storage materials, and heterogeneous catalysis. Thus, students will gain not only deep expertise in aerosol processes, but also a versatile methodological toolkit with impact far beyond climate and health, extending into energy, chemistry, and engineering applications.

What We Offer
•        Fully funded positions with competitive salary or stipend.
•        PhD program: normally 4 years.
•        Postdoc: initial 1-year contract, extendable up to 2–3 years upon mutual agreement.
•        Publishing in top scientific journals and build a strong career profile.
•        Excellent opportunities to validate your computational discoveries through close collaboration with strong experimental teams in the department.
•        Fully funded research visit opportunities to world-leading AI labs, AI for Science labs, and AI for Chemistry labs.

How to Apply
Please email the following materials to Dr. Huan Yang at huan.yang@mpic.de
•        CV
•        Bachelor’s transcript (PhD applicants only)
•        Short statement of interest (~1 page: motivation, background, and strength)
•        Contact information for at least two references (name, affiliation, email)
Tip: Use the subject line “Application – PhD/Postdoc – [Your Name]” and, if possible, combine materials into a single PDF.

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

Evaluation Procedure
•        Decisions will be made on a rolling basis until all positions are filled.
•        The first round of application review will begin between October to November 2025, and shortlisted candidates will be contacted during that month to arrange interviews.
•        We greatly value all applications and will make every effort to respond to serious candidates, though responses may not be possible in every case due to the high volume received.
•        The starting date is negotiable, but preferably no later than Autumn 2026.

About the Research Team

Prof. Yafang Cheng is 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 to study the life cycle of particles, from their molecular origins to global-scale consequences. Supported by world-class infrastructure — including the HALO research aircraft, high-end laboratories, and powerful computing resources — the department provides 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, PNAS, Science Advances, and Nature Communications). 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.

Dr. Huan Yang leads the Nanoscale Theory and Modeling team at the Max Planck Institute for Chemistry, focusing on molecular-level mechanisms of nanoparticle processes. His group develops computational frameworks that integrate quantum chemistry, molecular dynamics, mesoscopic transport theory, and AI-driven modeling to uncover how gaseous precursors evolve into aerosol particles. Dr. Yang received his PhD from the University of Minnesota under the supervision of Prof. Chris Hogan, pursued postdoctoral research at the University of Helsinki with Prof. Hanan Vehkamäki and Acad. Prof. Markku Kulmala, and was an affiliated researcher at the Italian Institute of Technology with Acad. Prof. Michele Parrinello. He was also selected as a Young Scientist for the Lindau Nobel Laureate Meeting 2025. His research, published in Nature Communications, Atmospheric Chemistry and Physics, The Journal of Chemical Physics, and Physical Review E, bridges fundamental theoretical advances with pressing environmental challenges.
The Aerosol Chemistry Department and the Nanoscale Theory and Modeling team aims to provide a collaborative, interdisciplinary, and international environment. We especially welcome motivated PhD and postdoc candidates with strong modeling, computational, or data-science skills, and those eager to connect theory with experiment. Joining our team means working on cutting-edge, globally relevant questions, contributing to high-impact publications, and developing versatile skills for a successful and influential scientific career.

Max Planck Institute for Chemistry
The Max Planck Society has a world-leading reputation as a science and technology research organization, with 39 Nobel Prizes awarded to its scientists, and is widely recognized as one of the foremost institutions for basic research. In 2020, the Nature Index ranked the Max Planck Institutes 3rd worldwide for publications in Nature journals. The Max Planck Institute for Chemistry is one of the oldest institutes of the Society, founded in 1912 as the Kaiser Wilhelm Institute for Chemistry in Berlin and relocated to Mainz in 1949. Its scientists, including Nobel Laureates Richard Willstätter, Otto Hahn, and Paul Crutzen, have made landmark contributions that shaped modern chemistry and atmospheric
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