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species' distributions. This project harnesses research in ecological and agent-based modelling, machine learning, and AI to increase the predictive power of models of species’ distribution shifts via “data
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methods dealing with model complexity - e.g., AIC, BIC, MDL, MML - can enhance deep learning. References: D. L. Dowe (2008a), "Foreword re C. S. Wallace ", Computer Journal , Vol. 51, No. 5 (Sept. 2008
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noting that techniques like Data Science and Machine Learning (ML) have been increasingly applied in analyzing the unprecedented amount of data collected by these platforms and systems, e.g., using
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Honors or Masters degree including substantial research project, GPA 80%+ from a reputed university Refereed publications including journal or conference of high repute Desirable Background in Machine
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Background in Machine Learning, Algorithms and Data Structures
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In this project, we will use machine learning methods to diagnose the health status of bee colonies and individual bees. Bee populations are threatened worldwide due to a number of factors
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distributions. We wish to represent the biological networks into proper formats, e.g., vector representations, so that existing machine learning algorithms (e.g., support vector machines) can readily be used
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inference and machine learning to develop subject specific mathematical models of the brain that can be used to infer brain states and monitor and image the brain. This work is centred around a
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for predictive analytics that incorporate modelling, machine learning, and data mining, we are building, analysing and modelling an individual’s baseline health profile against thousands (eventually millions
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information about behavioural patterns, but scoring this manually is time consuming. For this reason, machine learning solutions have been developed to automate behavioural prediction [5-12]. DeepLabCut [5] is