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will be involved in the development and implementation of novel behavioural and fMRI tasks and analyses, including tasks and analysis using AI language models (LLMs). The postdoctoral researcher will co
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from real world longitudinal data on management and health outcomes for children with mental health conditions. Methods have included deep learning, large language models (LLM), generative AI models (Gen
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Language Model (LLM) and their use in Model-Driven Engineering (MDE) and software architecture Demonstrated proficiency in MDE, including expertise in modeling, meta-modeling, model transformations, and
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-edge machine learning, including Large Language Models (LLMs), to enhance decision-making and planning in robotic systems. Qualifications: Applicants must have a PhD in Robotics, Control Theory
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, Robotics, Computer Vision, or related disciplines. Proven expertise and hands-on experience in one or more of the following areas: large language models (LLMs), end-to-end learning, AV localization
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Expert in advanced machine learning such as multi-agent generative AI, LLMs, Diffusion models, and traditional machine learning techniques Expert in CALPHAD-based ICME techniques Expert in combining
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seeking a postdoctoral researcher with expertise in distributed intelligence, reasoning models, LLMs, workflow management, and distributed systems to contribute to the development of advanced decentralized
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are considered meriting: Experience with research in close collaboration with industrial partners. Technical knowledge and applied experience in modern AI (including GenAI/LLMs) and software engineering
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communication skills Expertise in Generative AI: Strong background in machine learning, with specific experience in Large Language Models (LLMs), and Vision-Language Models (VLMs) Excellent programming skills
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tutors entirely on tutoring data, or exploring a wide range of approaches to fine tuning base LLMs) Other relevant themes, as proposed by the applicant. Mentors include Rene Kizilcec (primary mentor