{"id":161447,"date":"2025-09-01T13:12:52","date_gmt":"2025-09-01T11:12:52","guid":{"rendered":"https:\/\/www.cde.ual.es\/?p=161447"},"modified":"2025-09-01T13:12:52","modified_gmt":"2025-09-01T11:12:52","slug":"phd-generative-ai-and-machine-learning-sweden","status":"publish","type":"post","link":"https:\/\/www.cde.ual.es\/en\/phd-generative-ai-and-machine-learning-sweden\/","title":{"rendered":"PhD – Generative AI and Machine Learning – Sweden"},"content":{"rendered":"

<\/p>\n

Link\u00f6ping University is\u00a0looking for one PhD student working on generative AI\/machine learning, with applications towards materials science. Generative machine learning models have emerged as a prominent approach to AI, with impressive performance in many application domains, including materials discovery. This development has a huge potential for societal impact, with applications in renewable energy, energy storage, electronics, medicine, sustainable manufacturing, etc.<\/p>\n

The main focus for the advertised position is novel machine learning method development. However, you will be part of a larger cross-disciplinary research initiative involving both computer and material scientists, providing excellent opportunities for practical impact by taking the outputs from the developed machine learning models all the way to the materials discovery lab.<\/p>\n

From the machine learning perspective, your research will be in the area of generative models, geometric machine learning, dynamical systems, and\/or multi-modal learning. From the materials science perspective, our primary focus will be on ultra-thin, so-called, 2-dimensional materials. This class of materials has unique properties which make them promising candidates for next-generation electronic devices, energy storage systems, sensors, and catalysts. However, they also pose unique challenges from a machine learning perspective, calling for novel machine learning research that will push the boundaries beyond the current state-of-the-art.<\/p>\n

As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part. Your work may also include teaching or other departmental duties, up to a maximum of 20% of full-time. The work assignments also include actively contributing to the collaborative environment within which the project will be carried out.<\/p>\n

Qualifications<\/strong><\/p>\n

You have graduated at Master\u2019s level in machine learning, computer science, or a related area that is considered relevant for the research topic of the project, or have completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses in the subject areas mentioned above. Alternatively, you have gained essentially corresponding knowledge in another way.<\/p>\n

A successful candidate should have excellent study results and a strong background in mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience. Experience in applying or developing machine learning models for atomistic systems (in chemistry or physics) is advantageous. The applicant should furthermore have a strong drive towards performing fundamental research; the ability and interest to work collaboratively; and strong communication skills. The applicant should be able to communicate freely in oral and written English.<\/p>\n

Employment<\/strong><\/p>\n

The employment has a duration of four years\u2019 full-time equivalent. You will initially be employed for a period of one year. The employment will subsequently be renewed for periods of maximum duration two years at the time, depending on your progress through the study plan. The employment may be extended up to a maximum of five years, based on the amount of teaching and departmental duties you have carried out. Further extensions can be granted in special circumstances.<\/p>\n

Salary\u00a0<\/strong><\/p>\n

The salary of PhD students is determined according to a locally negotiated salary progression.<\/p>\n

Organization\/Company<\/b> \u2013Link\u00f6ping University.<\/p>\n

Research field<\/b> \u2013 Computer science.<\/p>\n

Research profile<\/b>\u00a0\u2013 First Stage Researcher (R1).<\/p>\n

Country<\/b> \u2013Sweden.<\/p>\n

Application Deadline<\/b> \u2013 29 Sep 2025 – 22:00 (UTC).<\/p>\n

More information:\u00a0Euraxess<\/a>.<\/b><\/p>\n","protected":false},"excerpt":{"rendered":"

Link\u00f6ping University is\u00a0looking for one PhD student working on generative AI\/machine learning, with applications towards materials science. Generative machine learning models have emerged as a prominent approach to AI, with impressive performance in many application domains, including materials discovery. 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