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Field
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capturing real human mental performance, constrained by limited time and subject to systematic errors, requires transforming rational models into process models that approximate probabilistic calculations in
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research using methods such as sensing technique, 3D printing, human-computer interaction, simulation, and/or machine learning to address challenges in machinery motion planning and construction safety
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for simulations, we aim to explore solution strategies to calculate the amount of water given meteorological data and map data. Here, in addition to traditional discretization methods such as finite elements and
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how people estimate quantities. The project combines theoretical modeling with behavioral experiments to advance our understanding of the cognitive processes that connect our sense of magnitude with
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aspects of health at NUS and in Singapore, and beyond; and to develop interdisciplinary translational research and evaluation methods to support our evidence-based approach. We aspire to become the leading
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Wellbeing Across Lifespan (SWAN) brings together expertise from the fields of medicine, life sciences, social sciences as well as computer and computational sciences to promote scientific and societal impact
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element method, wave propagation analysis, inverse problem, machine learning (ML), and artificial neural network. Strong background in research publications in the desired field. Experience mentoring other
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against existing commercial technologies. Key Responsibilities: Perform literature review on data available for commercial processes Provide estimations for data not available in literature Develop process
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | 19 days ago
to understand cumulative climate risks, specifically to produce hazard maps and multi-criteria hazard risk assessment methods integrating multiple parameters to estimate the spatial susceptibility of key climate
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Science and Technology. It aims to develop computational representations and methods for efficient sequential decision-making under uncertainty with applications in multi-robot domain. We will explore