Research Assistant - Machine Learning Applied to Gas/Surface dynamics (Closed)

In order to understand the complex dynamical process that take place in the interaction of gas particles with metal surfaces, the most accurate method consists in performing ab initio molecular dynamics (AIMD) simulations using density functional theory (DFT). However, the computational expense of AIMD often imposes some limitation on our ability to understand physical processes within a system. For example, many phenomena occur at timescales of tens of picoseconds which are often intractable in practice, considering that most of the time we need a lot of trajectories in order to have appropriate statistics.

In order to overcome this problem, the aim of this project is to construct multidimensional potential energy surfaces using atomistic machine learning algorithms trained with DFT data . This will permit to peform molecular dynamics simulations with the accuracy of DFT but at much lower computational cost, which will allow to increase both the number of computed trajectories and their total simulation time.

Knowledge and/or experience on machine learning methods and molecular dynamics simulations

• Estimated annual gross salary: Salary is commensurate with qualifications and consistent with our pay scales
• Target start date: 2025/10/06

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 "Gas/Solid Interfaces" Group, is a group formed by staff of the CFM, UPV/EHU and DIPC. The main researchers of the Group are Maite Alducin (CSIC), Iñaki Juaristi (UPV/EHU) and Ricardo Díez Muiño (DIPC). The work of the team is mainly devoted to the study of the dynamics of the elementary reactive processes that can occur when atoms or small molecules interact with surfaces, paying special attention to non adiabatic processes.

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: 2025/43
• Application deadline: 2025/07/15
• 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.