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/thesis: Industry-/collaboration PhD student in optimized off-road driving in forests Research subject: Soil science Description: We are looking for an industry/collaboration-based PhD student to develop a
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research activities, contributing to data collection and analysis, preparing reports or documentation, and collaborating closely with faculty, researchers, and students in relevant projects. This position
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that enable energy-intensive industries to plan and optimize their production based on energy demand, power consumption, and sustainability. The current PhD position focuses on analyzing how digitalization and
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contributing to sustainable forestry that creates value for both people and the environment. Read more about Södra here:https://www.sodra.com/sv/se/ Description: We are seeking an industry/collaborative PhD
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Further information Employment as a PhD student is limited to 4 years, teaching and other department duties may be added with max 20 %. Placement: Luleå, Sweden. Expected starting time: September 1, 2025
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. The research group led by Martin Enge is specialized in methodology-driven analysis of patient data, especially in the field of single-cell multiomics. We are a multidisciplinary group with expertise in both dry
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, iuliana.livia.dasu@fysik.su.se . For information about the employment Prof. Michael Odelius, odelius@fysik.su.se , who can also forward questions to the PhD student council at Fysikum. Application Apply for the PhD
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' at the Department of Physics of Stockholm University invites applications for a PhD position in theoretical physics, under the supervision of Maria Hermanns, with the subject 'Three-dimensional topologically ordered
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. Contact For more information about the project, please contact Prof. Markus Hennrich, markus.hennrich@fysik.su.se . For information about the employment Prof. Michael Odelius, odelius@fysik.su.se , who can
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This PhD position offers a unique opportunity to advance safe and transparent control for autonomous, over-actuated electric vehicles. You will work at the intersection of model predictive control