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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict
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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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; expertise in several of the following: life cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and
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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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Do you want to combine high-throughput directed evolution with machine-learning analysis of deep sequencing data to engineer better antibodies? The Sormanni Lab in the Department of Chemical
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segmentation, anomaly detection, and extraction of biomarkers for disease classification. Design deep learning models for multimodal image registration and alignment of ultrasound with ground-truth MRI data. 2
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various concepts in welding and related mechanical engineering processes and cleaning existing datasets. Creating deep learning models. Using deep learning to create a robust model that predicts welding
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different backgrounds, identities, and experiences are valued, and where our people are empowered to thrive through supportive leadership, shared responsibility, and a deep commitment to genuine care and
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where our people are empowered to thrive through supportive leadership, shared responsibility, and a deep commitment to genuine care and respect for our community. Find out more about our vision for a
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful