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particular, will be valued. Additional Information Eligibility criteria You can check the assessment criteria chart, click on the following link: https://sede.udc.gal/services/electronic_board/EXP2026/004170
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applications, agricultural monitoring and modeling, Agro-AI/ML, or digital twin. Instructions to Applicants: For full consideration, applicants must apply for the Research Assistant Professor at https
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integrative part of sustainable and healthy diets. Development of analytical methods, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12
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integrative part of sustainable and healthy diets. Development of analytical methods, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12
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of analytical methods, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12 PhD candidates will be based across seven different universities
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, with a view to developing and carrying out the above-mentioned project and related scientific activities, with a particular focus on the development of analytical models (data science – machine learning
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sponsorship Salary: Salary commensurate with education and experience Restricted: Yes Criminal Background Check: Yes About the Department: The Center for Spatial Information Science and Systems (CSISS) is an
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behavior of pests, their response to pesticides, including physiological and genetic mechanisms of resistance, innovations in applied pest management research (e.g., molecular, computational modeling
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that are inextricable from their social contexts but to embed community knowledge into health-care institutions, systems and models of delivery. Learn more about the CERC here: https://www.torontomu.ca/cerc-health
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, robust, and trustworthy robotic technologies. Your research will span core challenges such as robot control, decision-making under uncertainty, multimodal information fusion, and foundational models