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Field
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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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the division of Data Science and AI , we develop data-driven methods and AI solutions that support intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and
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. in Computer Science or a closely related field, with research focus in Reinforcement Learning, Machine Learning, Embodied AI, or AI for Education. Strong background in deep reinforcement learning, with
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leverage reinforcement learning, deep learning, and generative AI, and evaluate against the research front in mathematical optimization strategies, to enable efficient, robust, and adaptive evacuation
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be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and Stochastic Optimal Control”. Background or expertise in one or more of the following
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computational research; superior oral and written communication skills; experience with scientific publication; excellent interpersonal communication; demonstrated expertise in deep learning, preferably as
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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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cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and demonstrated ability to effectively
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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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funding has been secured through a prestigious grant from SciLifeLab, a Swedish national center for advanced research and one of Europe’s leading molecular biology laboratories, driving innovation in