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Hire Dr. Mortada T.

Lebanon

USD 60 /hr

Ph.D. AI Researcher | Graph ML, LLMs, Cybersecurity & Python

Profile Summary

I am a Ph.D. researcher in Computer Science with expertise in Artificial Intelligence, Machine Learning, Graph Neural Networks (GNNs), Federated Learning, and AI-powered cybersecurity. My work combines rigorous academic research with practical software engineering to develop scalable, reproducible, and production-ready AI solutions. My doctoral research focused on graph-based intrusion detection for IoT networks, resulting in peer-reviewed journal and conference publications. I have extensive experience designing complete machine learning pipelines, including data preprocessing, graph construction, feature engineering, model development, hyperparameter optimization, experimentation, evaluation, and scientific reporting. In addition to graph machine learning, I develop modern LLM applications, including Retrieval-Augmented Generation (RAG) systems, AI agents, prompt engineering workflows, evaluation pipelines, and FastAPI-based backend services. I am experienced with PyTorch, PyTorch Lightning, Hugging Face, LangChain, Weights & Biases, Docker, and reproducible research practices. My technical expertise includes: * Graph Neural Networks (GraphSAGE, GAT, GCN) * Machine Learning & Deep Learning * Large Language Models (LLMs) * Retrieval-Augmented Generation (RAG) * Federated Learning * AI-powered Cybersecurity * Python, PyTorch, FastAPI * Experimental design and statistical evaluation * Scientific writing and peer review Alongside my research, I serve as a university lecturer teaching Artificial Intelligence, Machine Learning, and Computer Programming. I have also co-supervised master's research projects and regularly mentor students and engineers in AI and software development. I am available for research collaborations, machine learning model development, AI consulting, literature reviews, experimental design, data analysis, scientific writing, reproducible research pipelines, and end-to-end AI software development. I enjoy working with both academic groups and industry teams to transform research ideas into practical, high-quality solutions.

Subject Matter Expertise

• ☆ Computer Science
• ☆ Information Technology
• ☆ Data Analysis
• ☆ Machine Learning
• ☆ Natural Language Processing
• ☆ Neural Networks
• ☆ Artificial Intelligence
• ☆ Deep Learning
• ☆ Computer Vision
• ☆ Cybersecurity
• ☆ Python
• ☆ Tensorflow
• ☆ LaTex
• ☆ Scikit-learn
• ☆ Markup Languages

Services

• Writing
• Research
• Consulting
• Data & AI

Writing Technical Writing, General Proofreading & Editing

Research Scientific and Technical Research, Systematic Literature Review

Consulting Scientific and Technical Consulting

Data & AI Predictive Modeling, Algorithm Design-ML, Data Cleaning, Data Processing

Work Experience

UNIVERSITY LECTURER

Notre Dame University-Louaize

January 2026 - Present

Education

PhD Computer Science

École nationale supérieure d'Arts et Métiers (ENSAM)

October 2022 - January 2026

Certifications

• PyTorch Ultimate 2024 - From Basics to Cutting-Edge

  Packt - Coursera

  https://coursera.org/share/bbb8b696c13931604725bdb902cfacb6

  February 2025 - Present

• Deep Learning Specialization

  DeepLearning.AI - Coursera

  https://coursera.org/share/3d996555fd6e947c401069b6d96ca74e

  December 2024 - Present

• Machine Learning Specialization

  DeepLearning.AI - Coursera

  https://coursera.org/share/74727c0ef7c6d935e5da63573b01341c

  March 2024 - Present

Publications

JOURNAL ARTICLE

Mortada Termos, Zakariya Ghalmane, Mohamed-el-Amine Brahmia, Ahmad Fadlallah, Ali Jaber, Mourad Zghal (2026). ADAP-GNN: Adaptive property-aware graph neural network for intrusion detection in IoT networks . Computers and Electrical Engineering.

Mortada Termos, Zakariya Ghalmane, Mohamed-el-Amine Brahmia, Ahmad Fadlallah, Ali Jaber, Mourad Zghal (2024). GDLC: A new Graph Deep Learning framework based on centrality measures for intrusion detection in IoT networks . Internet of Things.