A Doctoral Researcher (PhD student) in Machine Learning for Electron–Phonon Interactions and Wannier-Based Hamiltonians
Aalto University is where science and art meet technology and business. We shape a sustainable future by making research breakthroughs in and across our disciplines, sparking the game changers of tomorrow and creating novel solutions to major global challenges. Our community is made up of 16 000 students and 5 200 employees, including 446 professors. Our campus is in Espoo, Greater Helsinki, Finland. Diversity is part of who we are, and we actively work to ensure our community’s diversity and inclusiveness. This is why we warmly encourage qualified candidates from all backgrounds to join our community.The School of Chemical Engineering (CHEM School) is one of the six schools of Aalto University. It combines natural sciences and engineering in a unique way. The Department of Chemistry and Materials Science is looking for: A Doctoral Researcher (PhD student) in Machine Learning for Electron–Phonon Interactions and Wannier-Based HamiltoniansThe ELPH-ML project, led by Dr. Ransell D'Souza at the Department of Chemistry and Materials Science, Aalto University, and the Data-driven Atomistic Simulation (DAS) group, led by Prof. Miguel Caro at the Department of Chemistry and Materials Science, Aalto University, are jointly hiring a Doctoral Researcher. In this position, you will work on a project funded by the Research Council of Finland to build a machine learning framework linking electron–phonon interactions, Wannier-based Hamiltonians, and phonon properties for functional materials. You will work under the supervision of the Principal Investigator, Dr. Ransell D'Souza, and collaborate closely with Prof. Miguel Caro's group, whose core expertise is the development of machine-learning-infused atomistic modeling techniques and their application to important problems in chemistry, physics and materials science. Together, you will help advance a key scientific discipline that directly impacts important technological and societal topics such as thermoelectric energy harvesting and next-generation gas sensors. The project has access to state-of-the-art supercomputing facilities (CSC's Puhti, Mahti, and LUMI) and is well integrated within the international electronic-structure and machine learning communities. Informal inquiries about the position can be directed to Ransell D'Souza (rdsouza@sissa.it). Please read the description below in full before directly contacting us by email.Your role and goals You will develop data-driven and machine learning workflows to predict Wannier Hamiltonians, phonon properties, and electron–phonon coupling in layered transition-metal dichalcogenides (TMDCs) such as MoS₂, WS₂, MoSe₂, WSe₂, and WTe₂. For training the machine learning models, you will generate datasets from electronic structure theory calculations using Quantum ESPRESSO, Wannier90, and EPW. You will apply the developed E(3)-equivariant AI framework to quantify band-convergence effects on thermoelectric transport (Seebeck coefficient,...