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apply for third party funding. Contributions to teaching within the Engineering Geology group are also expected. Profile PhD in Data Science, Computer Science, Mechatronics, Remote Sensing, Engineering
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PhD in Computational Materials Physics or a related area is required. Experience with electronic structure calculations is essential. Familiarity with the use of machine-learning tools in materials
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modelling and/or empirical analysis. The research will ideally combine insights from economics and e.g. computer science. Profile Applicants should hold a PhD in Economics with a strong economic basis and
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progress Profile Required PhD in Computer Science with the focus on AI Proficiency in python programming Strong expertise in machine learning and deep learning frameworks (especially PyTorch) Demonstrated
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qualifications with a PhD in physics, electrical engineering, materials science or a related subject, and a background in magnetic thin films, nanostructures and spintronics. You should be motivated, proactive and
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with exam grading. - The anticipated start date is 1 September 2025 (earlier start dates can be considered). - A PhD in Mathematics (completed by the start date) is required. Please submit your
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the field of Computer Science or similar Solid background in the foundations of reinforcement learning Proven research experience with first-authored publications at peer-reviewed conferences (ICML
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of PhD and undergraduate students in the group. Profile You have a PhD degree in environmental sciences, environmental engineering, data science, or related fields, or expect to obtain such a degree in
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to refine models for specific forest ecosystems. Publish research findings in high-impact scientific journals and present them at conferences. Profile PhD in Remote sensing, Geoinformatics, Data Science
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quantum information science. Led by Prof. Wenchao Xu , the EQE group focuses on harnessing the potential of quantum systems involving neutral atoms and their Rydberg states for advancing quantum science and