IT systems

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Program Overview

The doctoral program is organized according to the LMD system (Bachelor-Master-Doctorate) over a three-year training period. It includes theoretical, methodological, and practical coursework, as well as supervised scientific research leading to the defense of a doctoral thesis.

The objective is to train researchers capable of contributing to scientific advancement and innovation in the field of computer science.

Teaching Language : French and English

Curriculum Highlights

Core Courses

Among the fundamental courses offered:

  • Network Architecture and Internet of Things
  • Artificial Intelligence and Machine Learning
  • Blockchain
  • Combinatorial and Swarm Optimization
  • Big Data
  • High Performance Computing

Additional courses include:

  • Scientific Research Methodology
  • Scientific Communication Techniques
  • Writing and Presenting Scientific Work
  • Research Ethics and Scientific Integrity


Advanced Topics

Advanced topics depend on the field of specialization. Examples include:

a)     Artificial Intelligence & Machine Learning

  • Swarm-Driven Deep Learning for Congestion Optimization in Traffic Networks
  • Privacy-Preserving Machine Learning-Based Data Processing in Edge Networks
  • Deep Learning Model for Forecasting and Detecting Abnormal Behavior in an IoT Environment
  • Artificial Intelligence Techniques for Medical Imaging
  • AI-Based Cyber Threat Detection
  • Real-Time Accurate Elderly Fall Detection Using Advanced AI Technologies
  • Precision Rheumatology: Leveraging AI for Personalized Treatment Response Prediction in Rheumatoid Arthritis
  • Exploring the Impact of Federated Learning in the Development of Smart Cities Applications

b)     Health & Medical Imaging

  • Anatomical Pathology and Artificial Intelligence
  • AI Techniques for Medical Imaging
  • Application of SIAgen for Generating Radiology Reports

c)     Intelligent Transportation & Autonomous Systems

  • Swarm-Driven Deep Learning for Traffic Optimization
  • Implementation of a Digital Twin to Enhance Drone Learning for Flight Object Tracking

d)     Cybersecurity & Cryptography

  • AI-Based Cyber Threat Detection
  • Smart Crypto System as a Future Alternative to Digital Financial Systems

e)     Smart Agriculture & Sustainable Development

  • Blockchain-Based Information Traceability in Agricultural Sectors
  • Multimodal Data Analytics – Applications in Smart Agriculture

f)       Embedded Systems & Networks

  • Energy Optimization in Safe Embedded Systems
  • Optimizing Network Resource Provisioning Using AI Techniques

g)     Smart Cities & Distributed Systems

  • Exploring the Impact of Federated Learning in Smart Cities


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Admissions Information

Eligibility: Hold a Master’s degree or an equivalent diploma.

Application: Application form, CV, certified copies of diplomas.

Selection: Based on the application and possibly an interview.

Once admitted: Registration is annual and subject to approval by the scientific committee.


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