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willing to work in a collaborative environment. Preference will be given to those with (i) strong background in quantitative methods, geospatial methods, AI and machine learning; (ii) experience in high
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. Strong (inter-)national network in field of application. Experience with high-performance computing (HPC) and large datasets. Experience with machine learning applied to geophysical signals. Experience in
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projects in data-driven nutrition, such as: statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome
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physics, or related fields). Advanced Training / Specializations: Radar. Computer Skills / Operation and Programming (required and proficiency level): Microsoft Office, advanced level. Foreign Languages
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9 Jan 2026 Job Information Organisation/Company Consejo Superior de Investigaciones Cientificas Department Centre for Automation and Robotics (CAR) Research Field Engineering Researcher Profile
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, Professor Position Number 600237 Vacancy Open to All Candidates Department EHH BSOM Physiology Department Homepage https://physiology.ecu.edu/ Advertising Department PHYSIOLOGY Division ECU Health/BSOM
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to the effective computation of the data and numerical simulation. Where to apply Website https://resurseumane.ase.ro/non-gaussian-self-similar-processes-enhancing-mathe… Requirements Research
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to the effective computation of the data and numerical simulation. Where to apply Website https://resurseumane.ase.ro/non-gaussian-self-similar-processes-enhancing-mathe… Requirements Research
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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.PEX and DOI identifier https://doi.org/10.54499/2024.15642.PEX ), financed by Fundação para a Ciência e a Tecnologia, I.P. through national funds under the 2024 Call for Exploratory Projects in All