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Field
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machine learning tools for the efficient analysis of the experimental data. For more information, visit our web page www.soft-matter.uni-tuebingen.de We are looking for a motivated PhD student to contribute
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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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, 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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Your Job: In this position, you will be an active part of our "Simulation and Data Lab Applied Machine Learning". Within national and European projects, you will drive the development of cutting
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equivalent university degree in psychology or a related discipline (computer science, neurosciences, biology, cognitive sciences) • Skills in perception psychology, neurosciences, machine learning, tracking
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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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future scenario simulation of VBD Including machine learning, statistical, and process-based models Present findings at scientific conferences and publish in peer-reviewed journals Contribute
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future scenario simulation of VBD. Including machine learning, statistical, and process-based models. Present findings at scientific conferences and publish in peer-reviewed journals. Contribute
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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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elements distribution, crystallographic texture), mechanical properties (hardness, yield and tensile strength) and corrosion profile (rate and localization). This work focuses on machine learning assisted