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

About the position

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

Institution: CNRS

Research Field: Chemistry

Researcher Profile: First Stage Researcher (R1)

Application Deadline: 22 Sep 2026 - 23:59 (UTC)

Job Status: Full-time

Is the job funded through the EU Research Framework Programme?: Not funded by a EU programme

Is the Job related to staff position within a Research Infrastructure?: No

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.

• 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.

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.

Years of Research Experience: None

Research Field: Chemistry » Computational chemistry

• 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.

Website for additional job details: https://emploi.cnrs.fr/Offres/CDD/UMR8060-THISTU-002/Default.aspx

What you’ll bring

• Eligibility criteria
• • 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.
• • 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.
• Research Field: Chemistry
• Education Level: PhD or equivalent
• Languages: FRENCH
• Level: Basic
• Years of Research Experience: None
• Research Field: Chemistry » Computational chemistry

At a glance

Position type

Research scientist

Institution

CNRS

Department

Institute of Chemistry for Life and Health Sciences

Research group

Not stated

Location

PARIS 05, France, FR

Supervisor / contact person

Not stated

Funding

Funding not stated
Is the job funded through the EU Research Framework Programme?: Not funded by a EU programme

Duration

Not stated

Expected start

Not stated

Vacancy reference

EURAXESS-463071

Research focus

Discipline

Chemistry, Physics

Research area

Surfaces & interfaces

Methods

DFT

Software

Python

Dates to know

Listed on FOSS Positions

17 Sep 2026

Original advertisement date

Not stated in the source

Application deadline

22 Sep 2026 · 23:59 UTC

Priority review date

Not stated

Last checked

17 Sep 2026

Last updated

17 Sep 2026

2026-09-22T23:59:00+00:00

How to apply

Application Deadline: 22 Sep 2026 - 23:59 (UTC)
• Application of the developed methods to first-row transition metal complexes, notably Ni, Fe, and Co.
Where to apply
Apply now

https://emploi.cnrs.fr/Offres/CDD/UMR8060-THISTU-002/Default.aspx

Original sources

https://euraxess.ec.europa.eu/jobs/463071 ↗

https://emploi.cnrs.fr/Offres/CDD/UMR8060-THISTU-002/Default.aspx ↗