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formally based at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science . At STIMA, we conduct research and education in both statistics and
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creative and stimulating environment with the opportunity to network with both the business community and international contacts. Read more about our benefits and what it is like to work at SLU at https
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solutions across the natural sciences. Your workplace You will be employed at the Department of Mathematics in the Division of Applied Mathematics, https://liu.se/en/organisation/liu/mai/tima . The research
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Innovative city . The position is formally based at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science . At STIMA, we conduct research and
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Engineering and will become part of the national graduate school FOFOS – Research School for the Transformation of the Public Sector (https://www.mdu.se/forskning/forskarskolor/forskarskolan-fofos ). FOFOS is
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experiments. For more information about our group and current projects, please visit https://qtech.fysik.su.se/ . This project is funded within the QuantERA II Programme that has received funding from the EU
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application! We are looking for a PhD student in Statistics with placement at the Division of Statistics and Machine Learning, Department of Computer and Information Science. Your work assignments As a PhD
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administrative and technical services—all within a setting that offers attractive employment conditions. To learn more about the department, please visit: https://www.umu.se/en/department-of-computing-science
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complex systems. Development and application of theoretical tools that combine experimental data and atomistic computer simulations to provide a comprehensive picture that is difficult to achieve through
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that support the unit for area protection and marine spatial planning, as well as operations at SLU Aqua. Your profile You have documented expertise in marine ecology and computer vision and machine learning