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. The successful applicant will possess a PhD or equivalent doctoral degree in the social sciences, including political science, public policy, political ecology, law, geography or another relevant field with some
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echocardiography dataset called CAIFE consisting of both healthy and abnormal fetal heart scans. You will be responsible for the design and testing of original machine-learning based methods for fetal heart
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will join a research group working on longitudinal models of multiple chronic diseases across the life course. The postdoctoral researcher will contribute to building and evaluating machine learning
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improve high-throughput experimental workflows including closed-loop thin-film optimization Apply AI and Machine Learning for data analysis and modelling Develop, improve and implement HW/SW concepts and
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(postdoc) Limited until: 31.10.2032 Reference no.: 6212 There are many good reasons to want to research and teach at the University of Vienna. And one is why around 7,700 academic staff members before you
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are also available. Applicants must have, or be about to obtain, a PhD in materials science, physics, or related discipline with experience in the design and conduct of experiments. Some experience with
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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Cognitive Science, Medical Informatics, or a comparable quantitative discipline Proven hands-on experience working with LLMs, foundation models, multimodal AI systems, or related machine-learning technologies
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Curious about opportunities for learning, creating, discovering, and innovating, and are encouraged to learn from failure. Show Your Fire by joining our team and exhibiting your passion and pride in your
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initiation latency, movement speed, gait characteristics, postural control, motor variability, and other behavioural descriptors. ESSENTIAL REQUIREMENT PhD in machine learning, artificial intelligence