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Field
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trustworthy machine learning, with a particular emphasis on mechanistic interpretability and its application to healthcare data. The successful candidate will contribute to understanding how modern machine
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, large language models, or computer vision is preferred Knowledge, Skills and Abilities: · Teamwork: Ability to collaborate with others and contribute to a team environment · Communication
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at the interface of biostatistics, machine learning, and biomedical data science. This mentored postdoctoral position is designed to support the development of an independent research trajectory in methodological
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of AI and Data Science : Machine and deep learning, NLP, BDI (Belief-desire-intention) systems, and Large Language Models (LLMs). Expertise in design and very good programming skills (Python, Pytorch
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-series analysis, and environmental data analysis. • Demonstrated experience in artificial intelligence, including machine learning and deep learning, applied to hydrological or water-related systems
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. Proficiency in SQL, Python/R, or similar tools; experience with big data platforms , machine learning, and data warehousing. Commitment to quality, integrity, confidentiality and compliance. Excellent
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of Engineering is seeking to hire a Post Doctoral Associate to conduct research in large-scale flood risk analysis. New York University (NYU) is one of the top private universities in the United States, and the
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for the captioned post. Duties and Responsibilities Develop and apply advanced artificial intelligence and machine learning models to real-world data (RWD). Create innovative tools and solutions to extract deeper
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. The successful candidate will be employed at the Department of Computer Science of the University of Luxembourg and have access to high-performance computing resources suitable for large-scale machine-learning and
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hydrological modelling, time-series analysis, and environmental data analysis. • Demonstrated experience in artificial intelligence, including machine learning and deep learning, applied to hydrological