230 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Iscte-IUL" positions in Switzerland
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space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where conventional
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. • Familiarity with machine learning, dimensionality reduction, clustering, and statistical modeling. • Strong communication skills, interest in interdisciplinary work, and ability to train students and postdocs.
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we seek to enhance resources for student learning on statistics and machine learning applied to these topics. Project background We would like to develop learning exercises that help students learn how
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stable water isotope data, and Statistical analyses, including machine learning approaches. The full-time position is funded for four years. Salary and social benefits are provided according to ETH Zurich
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contributes to positive change in society You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive
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with the Sinergia project partners You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive
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modeling of historical controls, as well as machine learning, data science, and epidemiological studies based on large SCI datasets. This is an excellent opportunity to contribute to translational research
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library. Strong interest in machine learning, reinforcement learning, and fluid dynamics. Ability to work independently and collaboratively in an interdisciplinary team. Excellent command of English, both
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Master’s students of national and international institutions Strong motivation to explore topics in human–computer interaction, learning technologies, and AI-assisted tools Experience or strong interest in
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to support a circular bioeconomy. We are seeking a highly motivated postdoctoral researcher to develop and apply machine‑learning and data‑science methods, with a focus on computer vision, to enhance wood