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. The goal is to contribute broadly to research on applications of AI in medicine, and in particular to the development and validation of novel computational language models, algorithms, and tools
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of classes, using Machine Learning (ML) techniques such as Decision Trees, K-Nearest Neighbors (KNN), XGBoost, Support Vector Machines (SVM), or Neural Networks. Explore and implement clustering algorithms
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and optimisation algorithms, focusing on their practical application in the context of the RaceEngineerAI project. Tasks include: - Developing models capable of simulating the behaviour of racing
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computer vision algorithms to detect clinical interventions performed by nurses and situations of agitation and risk of falling. Volume of data available for the project: Video capture in a hospital
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artificial intelligence-based algorithms to optimise operation and predict anomalies in water distribution networks. The algorithms developed should identify patterns and anomalies that indicate the presence
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OF THE WORK PROGRAMME AND TRAINING: 1) Development of workflows and algorithms to complement datasets of connected data spaces, to improve analysis results (forecasting, analysis of financial tools, predictive
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optimization and coordination algorithms for BC Hydro–cVPP interaction and evaluate grid performance through simulated case studies, and analyze service offerings and pricing via case studies and apply social
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recommendation mechanisms based on semantic analysis and natural language processing, with the aim of facilitating collaboration and convergence of proposals. Developing and training NLP algorithms in multiple
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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning algorithm for photovoltaic applications and utilising them for the investigation
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” wherein messages including emotions are incorporated into the movements of collaborative robots. Key Responsibilities: Design, implement, and test real-time multi-objective motion planning algorithms