Adaptive electronic-structure methods for predictive excited-state reactivity (M/F)

Job Description

Missions

The development of new quantum chemistry methods for describing and predicting the excited-state reactivity of transition-metal complexes. In particular, the project aims to improve spin-flip TDDFT-type approaches by combining them with machine learning methods, in order to enable a more reliable description of excited-state landscapes and to contribute to the rational design of new photocatalysts.

Activity

• Development and benchmarking of electronic structure methods based on MRSF-TDDFT theory.
• Development of adaptive density functional models, using machine learning techniques to optimize their parameters.
• Development and automation of computational workflows in quantum chemistry.
• Study of photoinduced mechanisms, potential energy surfaces in the excited state, and structure–property relationships.
• Application of the developed methods to first-row transition metal complexes, notably Ni, Fe, and Co.
• Analysis, interpretation, and dissemination of scientific results (publications, presentations, and conferences).

Your Profil

Skills

• Ph.D. in theoretical/computational chemistry, physical chemistry, or a closely related field.
• Strong expertise in quantum chemistry and electronic structure methods.
• Hands-on experience with DFT and TDDFT calculations and a good understanding of their theoretical foundations and limitations.
• Experience in modeling excited states and/or multiconfigurational systems is strongly desired.
• Proficiency in scientific programming, particularly in Python, and the ability to develop and automate computational workflows.
• Experience with quantum chemistry software and the use of high-performance computing (HPC) resources. • Demonstrated ability to independently lead a research project, critically analyze results, and develop new methodological approaches.
• Solid experience in scientific communication, as evidenced in particular by publications in international journals.
• Experience in machine learning applied to chemistry or physics is an asset.

Your Work Environment

The project will be carried out within the Chemical Theory and Modeling (CTM) group at Chimie ParisTech – PSL, in the i-CLeHS team, under the supervision of Thijs Stuyver. It is part of the development of a new research area at the intersection of quantum chemistry, computational photochemistry, and machine learning. The candidate will enjoy a high degree of autonomy and will be able to actively contribute to the scientific direction of the project and the development of new methodologies.

Constraints and risks

The position involves primarily computer-based work, with regular use of scientific computing resources. The role may occasionally require travel within France or abroad to attend conferences, thematic workshops, or scientific meetings. There are no specific experimental risks associated with the position.

Compensation and benefits

Compensation

3131 € brut/month, according to experience

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

The research professions