Flex Conference (Physical / Digital)

International Conference on Autonomous Systems with Machine Learning - (ICASML-26)

31st - 1st November 2026 , Dunedin - New Zealand

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Call For Papers

The (ICASML-26) emphasizes interdisciplinary collaboration by bringing together experts from diverse fields. It encourages research that integrates multiple perspectives to address complex global challenges.

Key areas such as Machine Learning are explored to promote cross-domain knowledge exchange and collaborative innovation.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Machine learning for autonomous navigation
  • Sensor fusion techniques in autonomous systems
  • Autonomous drones and machine learning
  • Safety challenges in autonomous systems
  • Machine learning for robotic control systems
  • Autonomous vehicles and AI technologies
  • Human-robot interaction using machine learning
  • Real-time decision making in autonomous systems
  • Autonomous systems in smart cities
  • Machine learning for environmental monitoring
  • Ethics of autonomous systems development
  • Autonomous systems for disaster response
  • Machine learning in autonomous manufacturing
  • Data-driven design of autonomous systems
  • Challenges in deploying autonomous systems
  • Autonomous systems for healthcare applications
  • Machine learning for predictive maintenance in robotics
  • Autonomous systems and cybersecurity concerns
  • Future directions in autonomous systems research
  • Integration of AI in autonomous technologies

Assessment

All submissions will undergo peer review to ensure quality and interdisciplinary relevance. Accepted papers will be presented and considered for publication in journals and conference proceedings.

Registration

Registering for the conference provides access to keynote sessions, technical presentations, and networking opportunities with global experts.

Publication

Publishing through the conference enhances the visibility of your research and connects your work with a broader academic audience.