Doctoral student in machine learning for sustainable welding materials

We are looking for a Ph.D. student to conduct ground-breaking research using machine learning methods with the aim of developing a new generation of welding materials. The project is multidisciplinary, and you will work closely together with experts at Chalmers and industry with long and recognized experience of welding science and AI methods. About us The Ph.D. student will be employed at the Division of Energy Technology at the Deptartment of Environmental and Energy Sciences at Chalmers University of Technology. We conduct research and offer education mainly in energy technology and energy systems. Our research focuses on combustion and gasification of biomass, technologies for carbon dioxide avoidance, development of energy materials, and sustainable energy systems. The current project will be carried out in close collaboration with the Division of Chemical Physics at Chalmers, which conducts fundamental research with respect to computational materials using first-principles methods and machine learning approaches. About the research project Welding is, in many ways, the backbone of our society and is prevalent across most industries, including automotive, energy, and manufacturing. Still, the industry is associated with high resource use and depends on certain strategic and critical metals. In this project, financed by the Swedish Energy Agency, you will work closely with industrial partners ESAB and Höganäs, with the overall vision of developing effective algorithms for rapid, robust predictions of welding materials. Who we are looking for The following requirements are mandatory: To qualify as a Doctoral student, you must have a Master's degree (masterexamen) of 120 credits or a Master's degree (magisterexamen) of 60 credits* in the fields of physics, chemistry, chemical engineering, data- or material sciences (or equivalent competence). Strong written and verbal communication skills in English Social competence is important as the position is interdisciplina