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- Institut de Físiques d'Altes Energies (IFAE)
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Field
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programming You have experience with modeling, statistical analysis and/or machine learning Experience with DNA- or RNA-sequencing data analysis is a plus Education and training You hold a PhD degree in
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platforms. Experience in development of digital twins or physics-informed machine learning models. Experience in programming (e.g., Python or equivalent) and development of control or data acquisition
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Experience developing pipelines and code for gravitational-wave searches and/or parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing
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infrastructure (e.g. Observatorio del Roque de los Muchachos) Hands-on training in cutting-edge techniques, from detector R&D to advanced data analysis and machine learning. Attendance to international
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=50415 Requirements Research FieldPhysicsEducation LevelPhD or equivalent Skills/Qualifications Advanced skills in Machine Learning and Artificial Intelligence Proficiency in spoken and written English
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of Spanish (not required but valued for teaching and policy dissemination in Spain). Experience with AI-based research workflows, machine learning techniques applied to financial data, or modern
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to the topic, including food safety, microbiology, computational biology, machine learning, artificial intelligence, data science, or other related scientific fields. Familiarity with data-driven
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, including Machine Learning Interatomic Potentials. • Other research experience will be considered. Personal Competences: • Strong commitment • Attention to detail • Demonstrated ability to work with deadlines
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development of analytical solutions, data analysis and machine learning. Candidates should have a demonstrated record of scientific publications in international journals and participation in conferences
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assisting with in-situ TEM measurements, facilitating cutting-edge research in sustainability and energy fields. Part of the project will also include the development of deep learning frameworks for TEM image