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The primary objective of this project is to enhance Large Language Models (LLMs) by incorporating software knowledge documentation. Our approach involves utilizing existing LLMs and refining them
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. Recent works on knowledge graph question generation [4,5] have mainly focussed on multi-hop questions. This project aims at developing novel methods that jointly address the challenging, dual problem
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compressed into lightweight student models using knowledge distillation, enabling efficient real-time inference on mobile devices. The distilled models will be deployed and optimized on mobile platforms, with
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project aims to devise novel methodologies for construct an event driven knowledge base to understand and detect criminal activities (eg - sexual grooming, or misrepresentation) in order to provide adequate
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perspective to fundamentally solve the central question: how should an observer act in an environment to actively uncover the goal of the agent? Required knowledge Proficiency in Programming, Bayesian
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nutritional data into a user-friendly platform, enabling consumers, restaurants, and policymakers to make informed food choices and reduce diet-related emissions. Required knowledge Data analytics and software
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their performance both empirically and through controlled user studies. Required knowledge Strong background in computer science in general Familiarity and understanding of basic principles underlying automated
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the social, cognitive, and communicative skills needed to autonomously engage in meaningful, long-term human-robot interactions. Project overview: Social robots are designed to be competent partners that help
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Candidates should hold a previous degree (Bachelor’s and/or Master’s) in Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering, with demonstrated knowledge in machine
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to create a model that, once trained on a broad and diverse set of time series data, can transfer knowledge effectively across tasks, enabling faster and more accurate insights without needing