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Field
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++, Python, and JavaScript languages, multi- and many-core SoC, RISC-V, hardware synthesis, hardware-software co-design, (meta-heuristic) optimization algorithms, machine learning frameworks, (bonus topics
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, spectropolarimetric inversion techniques, and machine-learning–based approaches, for the physical interpretation of solar images and spectral profiles. Special consideration will be given to applicants with experience
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CIIMAR - Interdisciplinary Center of Marine and Environmental Research - Uporto | Portugal | 25 days 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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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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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