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Ethics and Governance in Physical AI Development

 14 Hours
This instructor-led, live training in Nigeria (online or onsite) is aimed at beginner-level to intermediate-level participants who wish to explore the ethical, legal, and governance aspects of Physical AI development. By the end of this training, participants will be able to:
  • Understand the ethical challenges associated with Physical AI.
  • Identify key governance frameworks and regulations.
  • Develop strategies for responsible AI development.
  • Analyze case studies of ethical dilemmas in AI deployment.
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Human-Centric Physical AI: Collaborative Robots and Beyond

 14 Hours
This instructor-led, live training in Nigeria (online or onsite) is aimed at intermediate-level participants who wish to explore the role of collaborative robots (cobots) and other human-centric AI systems in modern workplaces. By the end of this training, participants will be able to:
  • Understand the principles of Human-Centric Physical AI and its applications.
  • Explore the role of collaborative robots in enhancing workplace productivity.
  • Identify and address challenges in human-machine interactions.
  • Design workflows that optimize collaboration between humans and AI-driven systems.
  • Promote a culture of innovation and adaptability in AI-integrated workplaces.
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Physical AI for Industrial Applications: From Concept to Implementation

 21 Hours
This instructor-led, live training in Nigeria (online or onsite) is aimed at intermediate-level participants who wish to explore the application of Physical AI in industrial settings, from conceptualization to implementation. By the end of this training, participants will be able to:
  • Understand the core concepts of Physical AI and its role in industrial innovation.
  • Design and simulate AI-driven physical systems for manufacturing and logistics.
  • Implement Physical AI technologies to optimize workflows and processes.
  • Assess the feasibility and scalability of Physical AI applications.
  • Navigate challenges in integrating Physical AI into existing industrial systems.
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Advanced Concepts in Physical AI: Designing Autonomous Systems

 21 Hours
This instructor-led, live training in Nigeria (online or onsite) is aimed at advanced-level professionals who wish to deepen their expertise in designing, programming, and deploying advanced autonomous systems. By the end of this training, participants will be able to:
  • Design advanced robotic systems with autonomous capabilities.
  • Implement cutting-edge AI models for decision-making and control.
  • Integrate and optimize real-time sensor data for enhanced performance.
  • Utilize advanced simulation tools for system testing and validation.
  • Address complex challenges in automation and deployment.
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Physical AI for Robotics and Automation

 21 Hours
This instructor-led, live training in Nigeria (online or onsite) is aimed at intermediate-level participants who wish to enhance their skills in designing, programming, and deploying intelligent robotic systems for automation and beyond. By the end of this training, participants will be able to:
  • Understand the principles of Physical AI and its applications in robotics and automation.
  • Design and program intelligent robotic systems for dynamic environments.
  • Implement AI models for autonomous decision-making in robots.
  • Leverage simulation tools for robotic testing and optimization.
  • Address challenges such as sensor fusion, real-time processing, and energy efficiency.
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Introduction to Physical AI: Building Intelligent Machines

 14 Hours
This instructor-led, live training in Nigeria (online or onsite) is aimed at beginner-level participants who wish to explore the fundamentals of Physical AI, including its components, development process, and hands-on implementation of basic intelligent machines. By the end of this training, participants will be able to:
  • Understand the principles and potential applications of Physical AI.
  • Design and prototype simple AI-powered robotic systems.
  • Implement basic AI algorithms for machine perception and decision-making.
  • Navigate and use tools like ROS for robotics development.
  • Integrate hardware and software to build functional intelligent machines.
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