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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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, mathematical and programming contexts Your research will include extending and contributing to models and codes, including both high- and low-level programming languages, e.g. Python/Matlab to the development
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corresponding knowledge in another way. A successful candidate should have excellent study results and a strong background in mathematics. The applicant should be skilled at implementing new models and algorithms
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) Research area: Large Language Models (LLMs), knowledge graphs (KGs), commonsense knowledge Tasks: foundational or applied research in at least one of the following areas: LLMs, KGs, knowledge extraction
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the development of a Virtual Training Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and
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collaborate closely with GGS partners across academia, industry, and government. Key responsibilities Developing life cycle inventories (LCA PhD) or material flow models (MFA PhD) in close collaboration with
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collaborate closely with GGS partners across academia, industry, and government. Key responsibilities Developing life cycle inventories (LCA PhD) or material flow models (MFA PhD) in close collaboration with
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comprehensive and trustworthy AI models for subtype prediction, significantly influencing clinical decisions and personalized treatment strategies. The current project funded by NWO is a continuation of the
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, Pennsylvania 19004, United States of America [map ] Subject Areas: Industrial Engineering Statistics Economics Mathematics Management Science & Engineering (more...) Computer Science and Electrical Engineering
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and accuracy, ultimately saving lives. This collaborative PhD project aims to develop and evaluate advanced deep learning models for speech and audio analysis to predict Category 1 emergencies