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skills in programming, modelling, and data analysis. Experience in formulating and solving mathematical optimization problems, as well as working on real-world demonstrators, is an asset. Proficiency in
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Research Assistant / HiWi (8 hours/week) - Climate Opinion Data Analysis with R - Faculty of Busines
analytics using R. Strong written and verbal communication skills in English. Familiarity with LaTeX is a plus. Must be currently enrolled in a master's program at the University of Basel. We offer you
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laser processing and to bring your ideas in AI/ML to the technology level. You have a solid background in programming (deep learning, reinforcement learning, etc.), electronics, high-speed data
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scanning probe microscopy (STM or AFM), ideally with a focus on magnetic nanostructures or spin-resonance techniques Programming skills in Python or similar languages A proactive and collaborative mindset
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events at the Department of Media Studies and the Digital Humanities Lab. You will be expected to play a leading organizational role in designing research and outreach programs and events. Your profile
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products is a strong plus. Excellent laboratory and organizational skills; ability to work independently and as part of a collaborative team. Good communication skills in English. In
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. The PhD defense must occur 24 months or less before the start of the appointment (tentatively summer 2026). Excellent written and oral communication skills in English. French is not a requirement. This is a
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if required. Proven ability to independently plan, execute, and troubleshoot NGS experiments. Strong multitasking skills with the ability to manage and prioritise multiple parallel tasks effectively
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good communication skills and the ability to work well in an interdisciplinary and international environment. Fluency in spoken and written English is a prerequisite.
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science, chemistry, electrical engineering or a related discipline Strong background in materials informatics and data science Proficiency in Python programming Experience in machine learning for materials