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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 2 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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Position Details Position Information Requisition Number S4947P Home Org Name 4H Program Division Name AL Cooperative Extension System Position Title 4-H Extension Agent - Bullock County Job Class
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of treatment, and patient and family education. Collaborate with other disciplines as appropriate to coordinate continuity of patient care. Implement treatment programs, which include the use of physical agents
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particular, the candidate will investigate enhancement of RL controllers in multi-agent settings and by incorporating optimization techniques such as multi-objective optimization. Overall, the candidate will
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for the future of mobile and satellite communications. Fields of applications range from 5G/6G telecommunications to satellite-based internet connectivity. For details, you may refer to the following: https
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Position Details Position Information Requisition Number S4947P Home Org Name 4H Program Division Name AL Cooperative Extension System Position Title 4-H Extension Agent - Bullock County Job Class
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techniques, dockerization mechanisms, and scripting. Experience with ROS, Python, C++, Docker and YAML scripts is necessary. The candidate will also conduct research in the broad area of autonomous multi-agent
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implementing multi-agent AI tools that: Assess and provide feedback on venture-related submissions Automatically generate pitch decks, financial projections, marketing strategies, and other materials to support
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agents or study interventions, as appropriate. Demonstrates expertise in participant assessment related to oncology (and related diseases) and treatment as evidenced by documentation and observation
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modeling and coordination of interdependent infrastructure systems and their subsystems (such as networks of transportation, gas, electricity grid), multi-agent reinforcement learning for distributed