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for the position are expected to have a PhD in Chemistry, Material Science or Chemical Engineering. Experience in the fields of material synthesis as well as the physical and electrochemical characterization
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materials for non-volatile memory applications. Your profile PhD degree in physics, materials science, chemistry, electrical engineering or a related discipline Strong background in materials informatics and
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, the candidate is expected to supervise BSc-/MSc students or semester interns. Your profile PhD in Materials Science, Mechanical Engineering, Manufacturing Technology or similar fields Experienced in CFD
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the experiments, advise/guide for long-term knowhow transfer Publish the results in peer-reviewed journals and present at international conferences Your profile PhD degree in Physics or a related field with a
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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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with surgeons, engineers, chemists and fiber technologists Your profile PhD in polymer science or biomedical engineering Hands-on experience in hydrogel development, bacteria-materials interaction is a
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the project's framework and communication of findings at consortium meetings, (inter)national conferences and in peer-reviewed journals. Your profile PhD degree in biology, toxicology, life science or a related
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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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are required to have: A completed PhD degree. Experience in machine-learning methods. Some skill in at least one of these topics: Large data sets analysis Statistics and uncertainty analysis (probabilistic