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deep learning to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models with capabilities such as conversational
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vocation for the design and development of large language models based on deep learning and capable of multimodal processing - information available in the CV and motivation letter; c) Mastery of
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and vocation for the design and development of large language models based on deep learning and capable of multimodal processing - information available in the CV and motivation letter; c) Mastery of
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relevant quantitative discipline (e.g. Bioinformatics, Computer science, Genetics/Genomics, Computational Biology or Data science). Experience programming in a high-level scripting language (R or Python
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culture, organoid differentiation and the programming language R is desirable. Furthermore, the candidate must have excellent organisational, communication and problem-solving skills. A successful track
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Natural Product biosynthesis experience advantageous. Evidence of publications in peer-reviewed journals. Capable in at least one programming language/environment (such as Python, MATLAB or similar
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. Experience with using the programming language R is desirable. The candidate must have excellent organisational, communication and problem solving skills. Evidence of a successful track-record in research and
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Language Education Resource Center (Daily Life Support in Okinawa) Submission Documents: - Curriculum vita in English (and Japanese if available); Cover letter in English (and Japanese if available); Names
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analysis, transport, financial markets, health and pandemic preparedness. AI4CI researchers at University of Exeter will develop “smart agent” tools that use large language models and generative AI
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at Monash. We encourage applications from Aboriginal and Torres Strait Islander people, culturally and linguistically diverse people, people with disabilities, neurodiverse people, and people of all genders