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, electrical & electronic engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine learning/analysis. Prior research experience
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Materials Generative Design and Validation Framework. The role will work at the intersection of machine learning, high-throughput experimentation, and materials discovery, focusing on accelerating the design
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: • Postgraduate qualification in Spanish as a foreign/second language, Spanish Linguistics or in Spanish studies. • Native proficiency in written and spoken Spanish to teach Asian adult students • Experienced and
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to develop and optimize scalable experimental protocols across diverse material families. This role is part of a multidisciplinary team integrating materials chemistry, machine learning, and autonomous
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Intelligence and Data Analytics in Air Traffic Management Systems. The selected candidate will work on developing innovative machine learning models to address key challenges in the future airspace system
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science, machine learning, artificial intelligence, or a related field. Candidates with a PhD may be considered for a Research Fellow position instead. Prior experience with video data visualization research will be
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Linux, with hands-on experience in data wrangling, pipeline development, and statistical analysis. Skilled in Machine Learning, including supervised and unsupervised methods for biological data
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in Computational Social Science, Deep Learning, Machine Learning, Large Language Models and/or Natural Language Processing. Possesses good knowledge in MS office, Python, statistical analysis using R
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writing/presentation Job Requirements PhD degree in an engineering field related to this project Experience in dynamic modeling, machine learning and optimization & controls Having basic knowledge in carbon
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Responsibilities: Conduct programming and software development for big data management. Design and implement machine learning models for optimizing graph data management. Conduct experiments and evaluations