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detectors and sources. § Implementation of QN component software agents into QN control manager. § Prepare high-quality scientific publications on performed work. § Present results to supervisors
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
opportunity This role offers an exciting opportunity to develop cutting-edge AI planning and orchestration technologies for Defence logistics, integrating multi-modal agentic AI systems and automated reasoning
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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. The modelling will
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reinforcement learning, generative learning (e.g., foundational models) applied to multi-agent systems and/or human-AI interaction. Grade 7 salary £45,103 This role is an open-ended contract with a funding end
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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. The modelling will
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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. The modelling will
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Inspection Service (APHIS), located in Manhattan, Kansas. The Animal and Plant Health Inspection Service (APHIS) is a multi-faceted Agency with a broad mission area that includes protecting and promoting U.S
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hospitals. PRIMARY DUTIES AND RESPONSIBILITIES: The qualified candidate will focus on developing new algorithms, including agentic artificial intelligence approaches, for the clinical integration
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methods/algorithms such as evolutionary and quality-diversity search, generative ML models, hybrid models, bio-inspired approaches, multi-agent systems Experience with real-time or embedded systems
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to their culture and pay our respects to their Elders past and present. View our vision towards reconciliation . Role highlights Apply large language models and multi‑agent AI to accelerate crop breeding Integrate