Research Assistant - Multireference diagnostics for DFT (Open)

Statistical learning has been recently applied to increase the predictability of quantum chemistry methods for various purposes. To effectively train artificial neural networks for various tasks, such as image recognition, natural language processing, and drug discovery, large-scale screening of extensive data sets is essential. Since molecular systems with a large multireference (MR) character compromise the performance of cost-effective methods such as density functional approximations, there is a need for MR diagnostics that can be universally applied to different electronic structure methods.

The purpose of this job is to design a machine-learning method that takes information from a KS-DFT calculation and provides information about the MR character of the molecule. Various ML models will be developed: a universal one, various functional oriented and a basis-set independent one. All these models will use INDmax (our in-house correlation diagnostics for ab initio wave function methods as a reference). The program will be implemented in FORTRAN and uploaded in GitHub. Extensive data sets for which the group already has benchmark data will be used for this purpose.

[1] Xu X., Soriano-Agueda L., López X., Ramos-Cordoba E., Matito E.; How Many Distinct and Reliable Multireference Diagnostics Are There? J. Chem. Phys. 162, 124102 (2025) [2] Xu X., Soriano-Agueda L., Lopez X., Ramos-Cordoba E., Matito E.; An All-Purpose Measure of Electron Correlation for Multireference Diagnostics. J. Chem. Theory Comput. 20, 721 (2024) [3] Via-Nadal M., Rodríguez-Mayorga M., Ramos-Cordoba E., Matito E.; Singling Out Dynamic and Nondynamic Correlation. J. Phys. Chem. Lett. 10, 4032 (2019) [4] Ramos-Cordoba E., Salvador P., Matito E.; Separation of Dynamic and Nondynamic Correlation. Phys. Chem. Chem. Phys. 18, 24015 (2016)

Bachelor in Chemistry. Strong background in Theoretical Chemistry, in particular in DFT and wave function methods. Programming experience in FORTRAN and Python. Knowledge of basic machine learning models such as Support Vector Machine and Rainbow Forrest.

• Estimated annual gross salary: Salary is commensurate with qualifications and consistent with our pay scales
• Target start date: 2026/06/17

We provide a highly stimulating research environment, and unique professional career development opportunities.

We offer and promote a diverse and inclusive environment and welcomes applicants regardless of age, disability, gender, nationality, ethnicity, religion, sexual orientation or gender identity.

The team is the Quantum Chemistry Development Group hosted at the DIPC (https://quantchemdev.github.io/)

Interested candidates should submit an updated CV and a brief statement of interest to the following application email below. 

Reference letters are welcome but not indispensable.

The reference of the specific opening to which the candidate is applying should be stated in the subject line, and the application must be received before the application deadline.

Although candidates are welcome to contact the project supervisors to know further details about the proposed research activity, please be aware that the application will be evaluated only if it is submitted directly to the email address listed below as application email.

• Reference: 2026/26
• Application deadline: 2026/06/05
• Application email: jobs.research@dipc.org

Applications received by the deadline will be evaluated by a Committee designed by the DIPC board on the basis of the following criteria:

• CV of the candidate (60%)
• Adequacy of the candidate’s scientific background to the project (20%)
• Reference letters (10%)
• Other: Diversity in gender, race, nationality, etc. (10%)

Evaluation results will be communicated to the candidates soon after. Positions will only be filled if qualified candidates are found.

The DIPC may revoke its decision if the candidate fails to join by the appointed time, in which case the position will be awarded to the candidate with the next highest score, provided it is above 50 (out of 100).

However, the selected candidate may keep the position if, in the opinion of the Selection Committee, the candidate duly justifies the reasons why he or she cannot join before the specified deadline, and as long as the project allows it.