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the use of modern machine‑learning methods within applied mathematics—particularly physics‑informed learning, anomaly detection, data‑driven modelling, and the construction of surrogate models grounded in
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. Additionally, you should address the "advantages" mentioned above, as these will be taken into account when ranking the candidates. the names and contact information for two referees. One of these should be
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financing by the University of Bergen. About the project/work tasks The fellow will be part of the existing research community at Statistics and Data science group. The research project for the fellowship
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how you meet each criterion. Additionally, you should address the "advantages" mentioned above, as these will be taken into account when ranking the candidates. the names and contact information for two
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, you should address the "advantages" mentioned above, as these will be taken into account when ranking the candidates. The names and contact information for two referees. One of these should be the main
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incorporating the symmetries and geometric structure underlying the data. This framework allows one to work effectively with data defined on domains that possess intrinsic shape or symmetry, such as articulated
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information about the appointment process is available here. Formal Regulations Applicants who have previously held a PhD fellowship at the University of Bergen are not eligible to apply for this position
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a vacancy for a PhD position at the Department of Information and Media Studies. The position is for a fixed-term period of 4 years, of which 25% will be dedicated to teaching, supervision and
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tailored for implementing relevant research tasks will be provided, particularly including practices following the data management plan from the group. Access to high-performance computing facilities and
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for analysis (including wet-sieving sediment samples, picking foraminifera under a microscope, cleaning, and weighing) Scientific data analysis and interpretation, also possibly in collaboration with climate