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upon future funding. Qualifications Required Education and Experience Appropriate PhD in a related field. Preferred Qualifications Experience with machine learning and deep neural network techniques
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) Corrosion behavior (electrochemistry & high-temperature oxidation) In-situ monitoring of AM processes Computational skills in: Phase-field modeling, Machine Learning, FEM, DEM, COMSOL Alloy design (CALPHAD
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language processing, machine learning, artificial intelligence, and human-computer interaction. Established within the School of Computer Science, LTI pioneers innovative ways to understanding, processing, and
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significant mathematical and computational background to learn and develop new AI methods. While demonstrated experience in computer vision and deep learning for pathology or other biomedical image analysis is
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qualified candidates to apply for a postdoctoral research position. The research will focus on statistical modeling, machine learning and trustworthy AI to develop effective and efficient algorithms
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Institute faculty, in areas such as: * Machine Learning and Computer Vision * Natural Language Processing and Data Science * Biomedical Informatics and Computational Neuroscience * Mathematical/Theoretical
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 2 months ago
holography. We are seeking a highly motivated postdoctor-al researcher to join our multidisciplinary team at the intersection of optics, electronics, machine learning, and atmospheric science. The successful
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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language processing, machine learning, artificial intelligence, and human-computer interaction. Established within the School of Computer Science, LTI pioneers innovative ways to understanding, processing, and
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analysis and machine learning methods for optimisation and decision making, to describe the F&V supply chains for various products at regional UK scale and assess their resilience to cascading risks