Murad Tarar

• Relocated to the Netherlands on Orientation Year Visa (Zoekjaar), utilising the Dutch graduate job-seeking visa programme to establish career in the Dutch engineering sector • Completed Nuffic/IDW degree equivalency evaluation for both MSc Advanced Robotics (QMUL) and BEng Mechatronics (NUST) for recognition within the Dutch professional framework • Completing advanced technical certifications including Siemens TIA Portal V20, Industrial Automation and PLC Specialization (Packt/Coursera)

• Designed and delivered structured robotics training programmes covering embedded systems (Arduino, Raspberry Pi), programming, control theory and autonomous systems • Conducted 20+ hands-on robotic workshops for STEM awareness

• Architected and commissioned Siemens TIA Portal-based PLC control systems for conveyor and intralogistics automation across 3 client sites (projects £50K+), delivered full project lifecycle from requirements through FAT/SAT to client handover • Integrated IIoT platform with cloud CRM using MQTT and Azure IoT Hub, delivered real-time operational visibility across client logistics networks reducing manual reporting by 40% • Led distributed multidisciplinary team across software, controls and mechanical disc

Initially undertaken part-time alongside MSc Advanced Robotics at Queen Mary University of London (Jul 2023 – Jan 2024), transitioning to full-time from February 2024 • Implemented automation strategy for inventory and logistics systems across a high-volume retail operation, reducing operational waste 28% and improving process efficiency 15% through systematic workflow automation and continuous improvement • Deployed cloud-integrated inventory tracking system enabling real-time stock visibility and predict

• Researched, applied for and secured admission to MSc Advanced Robotics at Queen Mary University of London, one of the UK's leading robotics programmes • Prepared for international relocation to the United Kingdom including visa application, financial planning and pre-course technical preparation in advanced robotics and control systems • Undertook independent study in robotics, advanced control theory and embedded systems to build readiness for postgraduate level study

• Led 120-person engineering unit responsible for repair and maintenance of 350+ military electromechanical systems • Commissioned full armoured vehicle repair facility incorporating overhead lifting, pneumatic tooling and embedded diagnostics, cutting maintenance downtime by 25% • Managed £250K+ procurement across 12 suppliers, achieving 11% cost savings through structured vendor negotiation • Improved system uptime by 35% and reduced equipment failure rates by 20% through root cause analysis and iterative

• Engineered real-time embedded control and sensor feedback architecture for weapon system automation, improving targeting accuracy by 45% • Built IoT-based fleet management platform with CMMS integration, eliminating 70% of unplanned downtime across a 350-vehicle fleet • Developed AI-enabled access control system using Raspberry Pi, TensorFlow and OpenCV, achieving 98.9% recognition accuracy and 90% reduction in unauthorised access incidents

• Programmed and integrated ABB industrial robots on multi-robot R&D platforms, developing trajectory planning and motion control algorithms in MATLAB, Simulink and Embedded C • Applied iterative prototyping methodology to embedded control development, shortening test cycles and improving system response across multiple robot configurations • Gained hands-on experience with industrial robot programming, sensor integration and real-time control in a research environment

Research Project: Designed and programmed a 5-DOF 3D-printed robotic arm with multi-control architecture including Bluetooth app, SD card automation, and manual interface. Courses: Robotics | Advanced Robotic Systems | Artificial Intelligence | Electronic Sensing | Digital Signal Acquisition and

Final Year Project: Developed an autonomous drone system for real-time animal tracking using APM 2.6, Arduino Nano, and OpenCV-based Android app for onboard image processing. Integrated PID/LQR controllers for flight stabilization and enabled Bluetooth-based manual/autonomous switching. Optimized