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for deep learning, speech, and audio research, including Aalto University’s large-scale scientific computing cluster with CPU and GPU nodes, access to CSC’s national computing infrastructure including LUMI
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. The position combines Empa UESL’s expertise in developing and accessing energy system models with the methodological expertise of the IMOS Laboratory in machine learning and foundation models. Postdoctoral
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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statistical and machine learning, deep learning, chemometrics, multimodal data fusion, computer vision, uncertainty-aware modeling, stochastic control, optimization, and deployable edge-to-cloud decision
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
world. Position Summary This Postdoctoral Research Associate will conduct advanced research in artificial intelligence, machine learning, computer vision, and medical image analysis. The position will contribute
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 6 hours ago
world. Position Summary This Postdoctoral Research Associate will conduct advanced research in artificial intelligence, machine learning, computer vision, and medical image analysis. The position will contribute
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minimum qualifications at the time of hire. PhD in computer science, data science, or related discipline Track record of publications in Artificial Intelligence and Deep Learning in peer-reviewed
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. The research will be carried out at the Department of Information and Communications Engineering, DICE, at Aalto University, Finland. The project environment offers excellent infrastructure for deep learning
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environments; (2) identify which patterns of student-AI interactions influence the adoption of deep or surface approaches to learning; (3) create, implement and evaluate guidelines and knowledge base
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learning and physics, addressing key challenges in modern quantitative biology. The successful candidate will be responsible for: • Develop and train deep learning models (CNNs, ...) data to predict IPLSs