Doctoral student in machine learning for sustainable welding materials

About the role

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 interdisciplinary, i.e., includes collaboration between two departments at Chalmers and industrial collaborators. *for students with an education earned outside of Sweden, a 4-year Bachelor's degree is accepted. The following experience will strengthen your application: Experience in using or developing machine learning or artificial intelligence algorithms. Experience with first-principle methods for material modelling, such as density functional theory and molecular dynamics. Experience of research or courses exploring thermodynamics, chemistry, or physics of materials such as metals and metal oxides.

Responsibilities

• Take courses at an advanced level within the Graduate school of Energy, Environment and Systems.
• Develop your own scientific concepts and communicate the results of your research verbally and in writing
• The position generally also includes teaching on Chalmers' undergraduate level or performing other duties corresponding to 20 percent of working hours
• Development of AI and ML models to establish composition-processing-property relationships in welding materials and to predict new material formulations. This includes both conventional predictive models and generative and active-learning approaches.
• The research will be conducted in close collaboration with industry, and we envisage that some research time will also be spent at industrial sites.
• Contract terms
• The Doctoral student positions are fully funded from start.
• The position is a fixed-term appointment of four years, with the possibility of teaching up to 20%, which extends the position to five years.
• A starting salary of 35,725 SEK per month (valid from May 1, 2026).
• Doctoral studies require physical presence throughout the entire study period. A valid residence permit must be presented by the study start date; otherwise, the admission may be withdrawn.

Benefits

Additional Information

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.

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Company Intel

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