Resourceful PLC Engineer specializing in the enhancement of industrial automation systems for optimized performance and reliability. Proficient in PLC programming languages such as Siemens S5 and S7-1200, with a particular focus on integrating IoT solutions using Node-RED. Adept in project management, collaboration, and client engagement, dedicated to delivering tailored solutions that exceed client expectations.

As a research intern at the Agency for Science, Technology and Research, I gained experience in designing, building, and running simulations of grasping control using the Pybullet module. I also developed an optimum gripper design and grasping control method for soft objects by implementing deep reinforcement learning. During my internship, I was able to enable a deep reinforcement learning model to learn hardware information across multi-variate robots by implementing a hardware conditioned policy, which led to a reduction in data samples required for training. These experiences have strengthened my skills in Python programming language and deep learning with PyTorch, and demonstrate my ability to develop innovative solutions to complex problems in the field of robotics.

As a PLC engineer intern, I was responsible for maintaining the material allocation algorithm for the conveyor system to transport material to the silo. This involved programming the PLC system to perform specific functions and ensuring that it operated efficiently during peak hours. As part of this process, I conducted thorough system testing and evaluation to identify any potential issues and optimize the system's performance. Through my experience, I developed a keen eye for detail and problem-solving skills that allowed me to efficiently operate and maintain the system.