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experimental studies, mechanistic modelling, time-resolved data analysis, and machine learning to develop and validate predictive models linking process signals to reaction behaviour, progressing from controlled
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and uncover yet unknown physiological particularities of sleep and other human health factors. Your Profile PhD (or near completion) in Biomedical Engineering, Computer Engineering, Computer
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-facing web GIS dashboard. Investigate the forest, landscape, and climate conditions that drive storm susceptibility, using major windstorms as natural experiments and interpretable machine-/deep-learning
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initiation latency, movement speed, gait characteristics, postural control, motor variability, and other behavioural descriptors. ESSENTIAL REQUIREMENT PhD in machine learning, artificial intelligence
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-augmented generation (RAG) approaches Systems and mathematical modeling of biological or complex systems Natural language processing and machine learning Data harmonization and integration Record of research
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challenge our students, staff and faculty to maintain such standards and we are delighted to have those join us, who can help us in our pursuits. Learn more about us by visiting our website at https
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distributed decision-making. Applicants must have a PhD in Electrical Engineering, Mechanical Engineering, Computer Engineering, Applied Mathematics, Mathematics, or a closely related discipline, and are within
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learning as well as a strong background in scientific programming (in languages like Julia, Python, Fortran or C/C++). The applicant must hold a PhD in physical oceanography, atmospheric sciences, computer
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data and deep learning methods to assess canopy cover, quality, carbon stocks, and ecosystem services. Mandatory requirements: PhD in areas related to forest resources, remote sensing, data science, or
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to industry and developments with Deep Learning (DL), Computer Vision (CV), Transformers, Large Language Models (LLMs), Natural Language Processing (NLP). Mandatory requirements • Bachelor's degree