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at the rank of Research Assistant Professor in applied probability, data science, machine learning, and spatial statistics. Candidates with a strong background in the development of novel models and original
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 16 days ago
establish reference performance and guide the design of more advanced models. The core of the work will then focus on developing and evaluating machine-learning-based NILM methods tailored
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the excellence cluster Your profile PhD in physics or in a related discipline experience in phase contrast or coherent X-ray imaging advanced programming skills in a high-level computer language (Python or related
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to PhD level in a technical subject, with relevant experience: Expertise in machine learning for audio, including speech, music and ambient sound generation Research publications in relevant areas, i.e
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preference over external candidates. For questions about the vacancy, you can contact: Claire Stevenson, c.e.stevenson@uva.nl . Where to apply Website https://www.academictransfer.com/en/jobs/357583/phd
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river valleys); • Identify stylistic patterns and regional variations in schematic rock art; • Apply machine learning tools for large-scale stylistic classification; • Establish a robust chronological
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, dimensionality reduction and/or machine learning methods (e.g., Lasso, ridge regression) is highly desirable. Familiarity with neurostimulation, Parkinson’s disease, or neuropsychological assessment tools is
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combinations of structural and functional properties, using both simulations for machine learning and experimental validation. Fabrication tools and methods are already established in our laboratory. Key
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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experimental workflows for generating and automating the acquisition of high-quality training datasets for machine learning models. Provide training to students on new technologies, protocols, and best practices