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, depending on your progress through the study plan. Starting date by agreement. Salary and employment benefits The salary of PhD students is determined according to a locally negotiated salary progression
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application! We are looking for a PhD student in Statistics and Machine Learning Your work assignments We are looking for a PhD candidate to work in the intersection of computational statistics and machine
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education in both statistics and machine learning, at the undergraduate, advanced, and PhD levels. We regularly publish solid contributions at the best machine learning conferences. STIMA is characterized by
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), offers an exciting opportunity to work at the forefront of AI security, tackling some of the most pressing challenges in the field. As a PhD student, you devote most of your time to doctoral studies and
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2026 - 12:00 (UTC) Country Sweden Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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two years at a time, depending on your progress through the study plan. Start as soon as possible or by agreement. Salary and employment benefits The salary of PhD students is determined according to a
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) within the Department of Computer and Information Science . At STIMA we conduct research and education in both statistics and machine learning, at the undergraduate, advanced and PhD levels. We regularly
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these may be taken into consideration. You must have a PhD in ethology or a related field who wants to contribute to our research on canine behaviour. Documented research experience and knowledge
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) Country Sweden Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a
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, at the undergraduate, advanced and PhD levels. We regularly publish solid contributions at the best machine learning conferences. STIMA is characterized by a modern view of the statistical subject, where probabilistic