Mohammed Abdelhadi Khater
Software Engineer | Full-Stack Developer | MSc Student in CPS
📍 Gaza, Palestine · 💼 Open to Remote Work Available
Software engineer with experience in web and mobile application development, and a strong interest in AI, IoT and Cyber-Physical Systems. Currently a master’s student in CPS, researching smart energy management for residential solar PV systems in the Gaza Strip.
About
I am a Computer Systems Engineer interested in building practical software for everyday needs. My background includes web development, backend programming, database-driven applications, and IT support. I enjoy turning requirements into clear, useful solutions for managing information and business workflows.
I am also completing my Master’s studies in Cyber-Physical Systems. My academic interests include the integration of software, IoT, and intelligent monitoring, particularly for resilient residential solar energy systems. My current research direction explores the use of Edge AI and IoT to improve energy management under resource-constrained conditions.
Skills
Core focus: PHP, WordPress, JavaScript, and database-driven web applications. Other technologies reflect additional experience or ongoing development.
Featured projects
See all projects →Sales Management System
Flutter-based project focused on sales and inventory workflows.
WordPress Like / Dislike Plugin
A plugin project for adding like and dislike interactions to website content.
BMS Flutter App
A cross-platform app project. Visit the repository for current implementation details.
Education & research
Research details →Master’s in Cyber-Physical Systems Engineering
Al-Aqsa University — Gaza, Palestine
B.Sc. in Computer Systems Engineering
Palestine Technical College — Deir El-Balah, Palestine
General Diploma in Education
Al-Aqsa University — Gaza, Palestine
CPS-Based Smart Adaptive Energy Management Framework for Improving Solar Energy Utilization in Gaza Strip
This research proposes a CPS-based framework combining IoT monitoring and AI-based forecasting to manage residential solar PV energy adaptively. It integrates solar generation and demand forecasting, battery-condition assessment, load prioritization, and smart load switching to improve use of available solar energy under changing, resource-constrained conditions.
Read the research overview →