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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
Area of research: Scientific / postdoctoral posts Starting date: 01.07.2025 Job description: Postdoc (f/m/d): Machine Learning for Materials Modeling With cutting-edge research in the fields
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students and PhD students. Applicants will have, or be close to completing a PhD in a relevant field and possess relevant experience, in the area of probability or statistical machine learning. They will
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Mohadeseh Taheri-Mousavi’s group. The postdoc will develop and conduct advanced machine learning techniques combined with computational research to study the mechanical behavior of welds. Responsibilities
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and Environmental Studies is looking for a highly motivated and independent individual to work as a Postdoctoral Associate. This position is for a post-PhD trainee preparing for a research scientist
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us to explore the relation between the degree and type of processing, and the foods that result from their use. The results will be used for data machine learning, in collaboration with other partners
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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain
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experience and profile: • PhD in machine learning theory, with thesis submitted before the start of the Postdoctoral position. • Publications on machine learning theory in proceedings of international machine
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The candidate will have a PhD or equivalent degree in bioinformatics, biostatistics, computational biology, machine learning, or related subject areas Prior experience in large-scale data processing and
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-reviewed publications. Prior experience with biological network analysis and practical application of a variety of machine learning and computer vision techniques is preferred. The successful candidate will
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Engineering, Mathematics, Statistics, Computer Science or conjugate subject; strong record of publication in the relevant literature; good knowledge of machine learning algorithms and/or statistical methods