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Field
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(25260475) Responsibilities: The Department is recruiting a scholar at the rank of Research Assistant Professor in computational mathematics, machine learning, scientific computing, statistics, and related
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the rank of Research Assistant Professor in computational mathematics, machine learning, scientific computing, statistics, and related areas. The appointee is expected to conduct high-impact research
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models to characterize agricultural and ecological systems; Experience in applying advanced Artificial Intelligence/Machine Learning (AI/ML) methods in agriculture (Agro-AI/ML); and Experience in
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. Lead and conduct research projects in data-driven nutrition, such as: statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and health data analysis of omics data
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early AGN; 2) Benchmark existing tools and develop Machine Learning -based approaches for improved source detection in WFI simulations; 3) Develop and test source characterisation and cross-matching tools
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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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in C++ and/or Python is expected, and experience in model analysis and parameter optimisation is beneficial. Experience in machine learning and neural networks is desirable. The successful applicant
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should hold a Master's degree in Computer Science, Artificial Intelligence, Computational Linguistics, Data Science, or a closely related field Solid background in machine learning and natural
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of Unix systems (GNU Linux) and keen to gain hands-on experience in Networks and systems Machine Learning knowledge is a plus Strong analytical and programming skills are required (Python, Matlab, Golang
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of the suitability of the profile for the functions and tasks to be performed focuses on the candidate's experience in machine learning and COMSOL modelling of materials and devices. Note: These criteria will be