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cybersecurity team at Luleå University of Technology and also our partner within Cybercampus Sweden, RISE. We will use methods and technologies including expert systems, evolutionary algorithms, machine learning
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patient clusters and digital phenotypes, leveraging machine learning approaches to identify individuals at high CV risk based on clinical and biochemical markers, immune markers, digital health data (e.g
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour
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, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will have experience in one or more of these subject
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to collect large-scale experimental datasets and apply machine learning techniques to predict the performance of complex food ingredients, supporting the creation of products with lower environmental
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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projects Designing for complex or high-consequence systems Human-centred product, service, or interaction design in healthcare, defence, or critical infrastructure Applied human-computer interaction or
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data requirements, and lower costs for large-scale modelling tasks. PINNs enhance predictive capabilities and efficiency by combining data-driven methods with physical principles. Unlike traditional
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Human Genome-Phenome Archive. The position will also be connected to a vibrant local ecosystem for data science and machine learning. Your Tasks The research group of Dr. Brian Clarke is looking for a