Engineering Physicist · Nuclear & Energy Systems · Intelligent Control

Canadian engineering physics graduate working at the intersection of nuclear energy, control systems, and applied AI. Experience spans nuclear power plant simulation, safety-critical control, embedded systems, and regulatory-facing research. Motivated by building reliable, real-world infrastructure where physics, software, and public trust meet.

Based in the UAE
LinkedIn: https://www.linkedin.com/in/dina-elhanan-66b6b0136
Github: https://github.com/digitaldina
Full CV: master-resume.pdf


Interests

  • Nuclear power systems & reactor fundamentals (CANDU, NPP operation, transient safety)
  • Control & automation (SCADA, PLCs, PID, safety-critical systems)
  • Physics-informed AI & simulation-driven decision support
  • Embedded systems & energy-aware software

Current Role

Nuclear Research Assistant — University of Sharjah
Sept 2025 – Present

  • Develop Python-based physics informed AI agents interfacing with nuclear plant simulations.
  • Participate in technical discussions with UAE regulatory stakeholders (incl. FANR) on feasibility, safety, and implementation of nuclear technologies.
  • Conduct stakeholder and public perception studies using structured elicitation and multi-criteria decision methods.

Selected Experience

  • Embedded AI Circuits Engineer, BricksSense — IoT energy monitoring, PID control, STM32 firmware
  • Research Assistant, McMaster (CERC @ MARC) — V2X systems, real-time data pipelines
  • Innovation Developer, RBC — Full-stack software in Agile environments

Education

B.Eng. Engineering Physics — McMaster University (2025)
Focused on nuclear power systems, control engineering, and energy infrastructure.

Capstone: Sustainable gravity-powered generator
Joseph & Amy IP Award for Innovation & Feasibility


Technical Skills

Nuclear & Control: NPP systems, SCADA, PLCs, PID, OPC-UA
Software: Python, C/C++, Java, TypeScript, SQL
Modeling & Tools: MATLAB/Simulink, FlexPDE, Ignition SCADA, Altium, ANSYS
AI & Data: TensorFlow, scikit-learn, simulation-driven analysis


Conferences & Recognition

  • Middle East Energy 2025
  • NSBE 50th Annual Conference
  • Quantum Cryptography School — University of Waterloo