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statistical software. Learning Objectives: Learn about the implementation of the application of machine learning methodologies in plant phenotyping and genotyping for the sugarcane molecular biology lab. Learn
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who shares our values and who will support the mission of the university through their work. Qualifications: Ph.D. in a relevant field such as Learning Sciences, Human-Computer Interaction, Education
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PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning for lung cancer imaging data; - identify and select the appropriate methods for the study in question; - develop
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following mandatory requirements: a) A completed degree in Computer Engineering; b) Good knowledge in the areas of Machine Learning, Natural Language Models, and Computer Security – information to be provided
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Postdoctoral Fellow to join the research lab in theoretical and computational neuroscience. The research will focus on how neural networks generate cognitive functions such as learning, memory, and decision
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results. Preferred Qualifications: Experience with generative AI deep learning and active involvement in data science and machine learning projects. Experience in neural network architecture, cloud
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or translational research experience Knowledge of machine learning, Bayesian modeling, or statistical method development Ideal Personal Attributes: Independent, proactive, and scientifically curious Detail-oriented
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programming language Experience with statistical inference or machine learning methods (e.g. ABC, Bayesian modelling) A proven publication record with at least one first author publication in a peer-reviewed
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Agrilus pest in Northern U.S.. Learning Objectives: Through participation in the research project, the participant will learn principles of classical biological control as well critical thinking skills and
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: Erlangen Programme for AI” This is a 5-year programme supported by the EPSRC and is a collaboration of mathematicians and computer scientists at the University of Southampton, the University of Oxford (lead