Doctoral Researcher (PhD student) in Machine Learning

Descrizione

Doctoral Researcher (PhD student) in Machine Learning Sintesi in italiano (traduzione automatica): L'Aalto University, situata a Espoo, Finlandia, cerca un Ricercatore dottorale (studente di PhD) in Machine Learning per il progetto ELPH-ML, focalizzato sulle interazioni elettrone-fonone e Hamiltoniani basati su Wannier. Il candidato lavorerà sotto la supervisione del Dr. Ransell D'Souza e collaborerà con il gruppo del Prof. Miguel Caro. Le principali mansioni includono lo sviluppo di flussi di lavoro basati su machine learning per prevedere proprietà fononiche e accoppiamenti elettrone-fonone, utilizzando calcoli di teoria della struttura elettronica. È richiesta una laurea magistrale in chimica (computazionale), fisica o scienza dei materiali. È preferibile avere esperienza in machine learning o Python, ma non è obbligatoria. Il progetto offre accesso a strutture di supercalcolo all'avanguardia e opportunità di sviluppo accademico per una carriera internazionale. 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 Hamiltonians The 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, conductivity, ZT) and to model gas adsorption effects (NH₃, CO, CO₂) relevant to next-generation 2D gas sensors. You will manage large-scale simulations run on world-class supercomputing facilities alongside AI algorithms and data analytics tools, and share your results with experimental collaborators. The position is part of the Research Council of Finland project ELPH-ML ( https://research.fi/en/results/funding/88752 ). In combination with academic development courses at Aalto University, we will help you grow a competitive and international career profile. Your experience and ambitions We welcome candidates with a Master's degree in (computational) chemistry, physics, or materials science who are curious about applied machine learning in the natural sciences. Prior machine learning or Python experience is a strong bonus, but not a must. We seek colleagues who enjoy coding, scripting an Annuncio in inglese.