Shivam Sharma

Doctoral Candidate at University of Auckland

Auckland, Auckland, New Zealand

About

I am a PhD candidate in Mechatronics Engineering at the University of Auckland, specialising in intelligent manufacturing systems and machine learning for advanced machining processes. My research focuses on developing self-learning CNC machine tools that use sensor data and machine learning to understand machining conditions, optimise process parameters, and continuously improve their performance over time. By integrating data-driven intelligence into manufacturing systems, my work aims to enable more autonomous, adaptive, and efficient machining for next-generation smart factories. My broader research interests include smart manufacturing, machine learning, digital manufacturing, cyber-physical systems, and industrial automation. I am passionate about translating research into practical engineering solutions that improve manufacturing capability, productivity, and sustainability.

Experience

  • Research and Development Engineer Intern at TRACKIT LIMITED
    Nov 2024 - Feb 2025 · 4 mos

  • UoA Department of Mechanical and Mechatronics Engineering ()
    • Part 2 Mechanical & Mechatronics Mentor
      Mar 2024 - Oct 2024 · 8 mos

      This role involves providing extensive and personalized guidance to second-year mechanical engineering students, ensuring they learn more about their specialisation and excel in their studies. Additionally, it includes actively soliciting their feedback on course content and structure to continually improve the curriculum. By fostering an interactive and responsive environment, this position aims to enhance the overall learning experience and address any concerns or suggestions from the students.

    • Summer Research Student
      Nov 2023 - Feb 2024 · 4 mos

      During the summer, I dedicated 10 weeks to an intensive research project titled "Acoustic Black Hole: Does It Really Work as It Sounds?" This project involved a comprehensive process that included the design, manufacturing, and testing of acoustic black holes. My work required developing detailed CAD models, fabricating prototypes using different materials and techniques, and conducting a series of experiments to evaluate their effectiveness. The research aimed to explore the practical applications and theoretical underpinnings of acoustic black hole technology, ultimately contributing valuable insights to the field.