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Field
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machine learning approaches to quantitatively analyze experimental data and predict emergent multicellular behaviors under varying mechanical and chemical environments. For more information about our lab
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elements distribution, crystallographic texture), mechanical properties (hardness, yield and tensile strength) and corrosion profile (rate and localization). This work focuses on machine learning assisted
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environments Practical experience with machine learning and AI methods and an interest to learn, adapt and apply ML methods to challenging problems in mass spectrometry. Independent and cooperative working in
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test non-invertibility using machine learning attacks Protocol design. You conceptualize a proof of physical work protocol and design attack strategies against it Blockchain simulation. You simulate a
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, storage, accessibility/sharing, archiving, publication, and preparing data for machine learning applications. The Research Training Group RTG 3120 offers, subject to the availability of resources, a
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for the ERC Advanced Grant project “Equilibrium Learning, Uncertainty, and Dynamics.” **Positions Available** We invite applications for Doctoral Researchers with a strong background in machine learning and an
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analysis (e.g., econometrics, statistics, machine learning) A high motivation and the ability to work independently with a strong team orientation Excellent spoken and written English and the will
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, agricultural sciences with a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a
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with deep learning frameworks (e.g., PyTorch, TensorFlow) is highly desirable strong interest in interdisciplinary research combining imaging, machine learning, and porous materials strong analytical and
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, prototyping, programming (device communication, databases) Experience in the following areas is also a bonus: electrocatalysis, rheology, coating technology, machine learning Intrinsic motivation to show