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of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of
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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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experimental studies, mechanistic modelling, time-resolved data analysis, and machine learning to develop and validate predictive models linking process signals to reaction behaviour, progressing from controlled
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application! Work assignments You will join the research project Countering Bias in AI Methods in the Social Sciences, a collaboration between the Institute for Analytical Sociology (IAS) at Linköping
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. The postdoctoral researcher(s) will join an international research environment at Umeå University, including Stat4Reg (www.stat4reg.se ), which develops statistical and machine-learning methods for register data
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. The research tasks will include to develop machine-learning methods using experimental data provided by collaborating experimentalists. A central part of the work will be to identify and define the most relevant
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activities within the group, leading collaborative tasks in national and international research projects, publishing results in leading journals and conferences, and supervising MSc students and co-supervising
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industry links and diverse environment create a collaborative setting where ideas grow into real impact. At the division of Data Science and AI , we develop data-driven methods and AI solutions that support
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, spans the breadth of computing disciplines. Our internationally visible research, strong industry links and diverse environment create a collaborative setting where ideas grow into real impact. At