PhD Candidate in AI-Driven Computational Modeling of Catalytic Mechanism Norwegian University of Science and Technology in Norway

The Norwegian University of Science and Technology (NTNU) invites applications for a PhD position in AI-Driven Computational Modeling of Catalytic Mechanism, based in the Materials Theory group at the Department of Physics in Trondheim, Norway. This opportunity is part of the DYNCAT project, funded by the Research Council of Norway, which aims to develop advanced, physics-based and AI-enhanced computational models to study and optimize the Rochow-Müller process—the industrial method for producing raw materials for silicone manufacturing. The project focuses on improving production efficiency and control through particle-based simulation techniques and data-driven modeling, with a particular emphasis on understanding catalytic mechanisms and the formation of dichlorodimethylsilane (M2), the key product of the Rochow-Müller reaction.

The successful candidate will join an international research environment and contribute to computational modeling efforts within the Materials Theory Group. The position involves developing and applying artificial intelligence (AI) and machine learning (ML) methods for modeling catalytic mechanisms relevant to silicon formation. You will create AI/ML-driven computational workflows that leverage atomistic and electronic structure simulation data (such as Molecular Dynamics, Monte Carlo, and Density Functional Theory) to generate high-quality training and validation datasets. Integration of AI/ML models with particle-based simulations will enable predictive and multiscale descriptions of catalytic processes. Benchmarking and validation of AI/ML predictions will be performed through systematic comparison with DFT- and MD/MC-based results, as well as experimental trends and literature data. High-performance computing (HPC) simulations will be used to analyze and visualize time-dependent structural and dynamical properties.

Collaboration is a key aspect of this position, with active engagement alongside research scientists at SINTEF Industry, who bring expertise in experimental and theoretical catalysis, surface science, and adsorption processes. The PhD position is for three years, with the goal of completing a doctoral education leading to the award of a PhD degree. Supervision will be provided by Associate Professor Raffaela Cabriolu (NTNU) and Dr. Francesca L. Bleken (SINTEF Industry).

Applicants must hold a Master's degree or equivalent in Physics, Computational Chemistry, or a related engineering subject, and be qualified to pursue a PhD in physics. Documented experience or formal training in AI/ML methods is required, along with a strong academic background (average grade B or better on NTNU's scale). Admission to the faculty's Doctoral Programme is mandatory. Fluency in spoken and written English is required, and applicants from non-English-speaking countries outside Europe must provide documentation of English skills. Preferred qualifications include a background in computational science, physics, materials science, or related fields, strong programming skills (C/C++, Python, Julia), familiarity with atomistic simulation methods (MD, MC, DFT), and motivation for AI-driven modeling of physical and chemical processes.

The position offers a gross annual salary of NOK 550,800, with a 2% statutory contribution to the State Pension Fund deducted. The employment period is three years, and the successful candidate will have access to employee benefits and career guidance. NTNU values diversity and encourages applications from candidates of all backgrounds. The application must be submitted electronically via Jobbnorge.no, including all required attachments (CV, transcripts, diplomas, motivation letter, Master's thesis, publications, and referee contact information). The deadline for applications is January 9, 2026.

For further information, contact Associate Professor Raffaela Cabriolu at [email protected]. More details about NTNU and living in Trondheim can be found on the university website.