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Field
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Intelligence/Machine Learning (AI/ML) methods in agriculture (Agro-AI/ML); and Experience in programming with multiple languages (e.g., Java, C/C++, Python) for geospatial information systems, agro-informatic
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-good university degree in economics - strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) - a high motivation and the
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, statistics, machine learning) - a high motivation and the ability to work independently with a strong team orientation - excellent spoken and written English and the will to acquire a certain working language
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CIIMAR - Interdisciplinary Center of Marine and Environmental Research - Uporto | Portugal | about 1 month ago
highly motivated and oriented towards research in computational biology applied to microbial genomics and natural products biosynthesis. Experience in machine learning and the application of artificial
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on psychological assessment of learning and attention disorders and neuropsychological testing with youth with various medical conditions (including seizures, cancer, and kidney transplant patients). The fellows
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are encouraged to Be Curious about opportunities for learning, creating, discovering, and innovating, and are encouraged to learn from failure. Show Your Fire by joining our team and exhibiting your passion and
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· Willingness to learn mouse protocols for cancer research. · Qualifying competencies include excellent oral and written communication skills. · Excellent self-motivation, organizational skills, creativity
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or qualification, field of scholarship, and accomplishments in the field. Minimum Number of References Required 2 Maximum Number of References Allowed 3 Keywords Machine Learning Reinforcement Learning Foundational
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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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the rank of Research Assistant Professor in computational mathematics, machine learning, scientific computing, statistics, and related areas. The appointee is expected to conduct high-impact research