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on developing hybrid traffic flow models that combine physical modelling principles with machine learning approaches, such as Physics-Informed Neural Networks (PINNs) and machine-learning-enhanced traffic models
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Assistant for Research and Teaching (F/M) – 1 FTE (Division of Intelligent Decision Support Systems)
analysis, machine learning, neural networks, information retrieval, and introductory artificial intelligence, fluent command of both Polish and English, with the ability to teach courses in English-language
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conformal mapping. Minimal technical preferences include: expert level proficiency in programing (Python, C++), advanced proficiency in neural network model development, training, and validation, advanced
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predictive maintenance in chemical plants. Key Responsibilities: Create and implement hybrid AI models that merge machine learning techniques with mechanistic frameworks (like physics-informed neural networks
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on Artificial Neural Networks and Gaussian Process modelling, to accelerate processing optimisation. Consolidate experimental, techno‑economic, and sustainability data into robust technical evidence packages
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | about 1 month ago
written) skills, scientific judgment, and attention to detail. Assets Experience with hybrid physics-AI approaches, physics-informed neural networks, or AI-augmented parameter estimation in ocean or climate
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applications from candidates of all backgrounds. Website for additional job details https://biomed.au.dk/transcend-network Work Location(s) Number of offers available1Company/InstituteUniversity of Porto (UPORTO
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Knowledge of Physics-Informed Neural Networks (PINNs) or equivalent methodologies. o Experience in the acquisition, processing, and analysis of data from sensors or industrial testing. o Participation in
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Information Management, or related fields; Have basic knowledge of machine learning models in supervised and unsupervised learning tasks (i.e., k-nearest neighbours, Decision Trees, Neural Networks, Logistic
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-Informed Neural Networks (PINNs) and hybrid models that respect the physical laws governing the real-world system Applying Deep Reinforcement Learning (DRL) algorithms to optimize processes within simulation