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basic principles of reinforcement learning and popular modern reinforcement learning algorithms. Students will develop familiarity with both model-based and model-free reinforcement learning algorithms
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Japan and North America, to improve an existing model for the spread of Cyvirus cyprinidallo3 (also known as Cyprinid Herpes Virus or CyHV-3) as a biocontrol agent for common carp in Australia
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About the Opportunity The successful candidate will contribute to an ambitious project developing perceptual AI agents that assist humans in daily activities through behavioral understanding and
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-based modelling). Flexibility to travel and engage fully in academic and non-academic secondments abroad Specific Requirements As per the specific eligibility criteria of Marie Sklodowska-Curie Actions
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classes and their roles in scientific applications, such as deep neural networks (DNNs), convolutional neural networks (CNNs), transformer models, and graph-based neural networks. Familiarity with software
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Python, PyTorch, and Linux/command line Familiarity with LLM in-context learning and prompt engineering Basic understanding of modern LLM models, ecosystems, and pipelines, including retrieval-augmented
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discovery, or scientific knowledge extraction. Experience building or studying autonomous or semi-autonomous coding agents. Experience designing or implementing automated experimentation or simulation-based
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already been awarded a PhD degree. Selection process You should submit your CV through a dedicated site: https://cv.newton-6g.eu/ Additional comments Position: Data-driven models for CF networks
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cyber resilient AI, promoting education, knowledge sharing, and policy development. This PhD position is associated with the research theme Runtime Security Assurance. Once trained, AI models can be
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The Faculty of Engineering, Department Electronics and Informatics, Research Group Electronics and Informatics: Research – Development - Innovation is looking for a PhD-student with a doctoral grant