Hassane AZZI - Backend Development Engineer C++ / Java / Python

Professional Profile
Computer Science Engineer, option: Operations Research & Optimization (M2INFO_RO - 2016) from ENAC (Ecole Nationale de l'Aviation Civile), I apply my expertise in operations research, advanced algorithms, and software development (C++, Java, Python) to projects across various industrial sectors. My combined scientific and technical skills enable me to develop robust and optimized solutions tailored to challenges of performance, reliability, and operational efficiency.

Experiences

As part of a temporary increase in activity for various projects with the Naldeo company, my role during this mission is to support different clients (Omexom, Valorem and Urbasolar) in the development of optimal management solutions for hybrid energy systems in non-interconnected zones (ZNI), in particular overseas departments and regions (Corsica, Martinique, Guadeloupe, Reunion Island and Mayotte). This advanced energy management solution is capable of maximizing the revenues of hybrid power plants, by optimizing production in real time according to the announced program, the actual state of the plant and the updating of production forecasts.

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Software Development Engineer | C++ Back-End Developer

SCLE SFE

March 2022 to April 2024

Full-time

Toulouse

France

For the client SCLE SFE, and within the "Reliability" team, the mission consists of developing a software application (Vcard) for simulating high-voltage substation cards.

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As part of the continuation of the internship carried out in Cherbourg for the E3S (Energy Smart Sailing Ship) project, this assignment involved completing the development phase of the project.

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Mission: As part of the E3S (Energy Smart Sailing Ship) project led by the Naval Engineering and Energy Research & Innovation Unit at SEGULA Technologies, the objective of this mission was to implement a hybrid intelligent energy management system (EMS) capable of managing the production and consumption flows of electric energy on board a long-distance cruising sailboat.

The sailboat is equipped with renewable energy sources (solar panels, wind turbines, a hydrogenerator, a diesel generator, and a battery bank) installed on board, ensuring safety during navigation and maximum comfort for passengers (air conditioning, internet, television, shower, etc.).

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Resolution of air traffic conflicts using exact and approximate methods (metaheuristics)

École Nationale de l'Aviation Civile (ENAC)

October 2016 to January 2017

Student Project

Toulouse

In air traffic management, separation distances must be respected to avoid any risk of collision between aircraft. Two aircraft are considered to be in conflict if the distance separating them is less than 5 nautical miles (NM) horizontally or 1,000 feet (ft) vertically. In other words, an air conflict corresponds to a loss of separation between two or more aircraft that find themselves too close to each other, in violation of predefined safety standards. The resolution of air conflicts is based on the implementation of avoidance maneuvers, such as changes in speed, altitude, or heading, in order to reestablish minimum separation distances. Each maneuver generates a cost, particularly in terms of kerosene consumption, depending on its nature. The objective of this project is precisely to minimize the total cost of maneuvers applied to aircraft, while ensuring compliance with safety standards.

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Scheduling of landings at an airport

École Nationale de l'Aviation Civile (ENAC)

October 2016 to January 2017

Student Project

Toulouse

Airport traffic management poses many optimization challenges, making air traffic difficult to predict. In this context, assigning parking stands to aircraft, finding optimal landing sequences on one or multiple runways, as well as planning strategic taxi routes are major issues for air navigation services.The goal of this mini-project is to develop a flight sequencing strategy for arrivals at airports to avoid congestion caused by closely spaced aircraft arrivals.

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This project aims to develop and implement a Machine Learning algorithm able to detecting anomalies in a system, using novelty detection and outlier detection techniques.

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Case study and application to solve real-world industrial problems: Knapsack problem, Vehicle Routing Problems (CVRP) applied to logistics.
Modeling and solving optimization problems using solvers
Technical Environment: IBM ILOG CPLEX Optimization Studio, LocalSolver, Pyomo, Mathematical Modeling, Combinatorial Optimization, Linear Programming

Skills

Operating systems (OS)

Linux (Ubuntu, Debian, Red Hat), Windows, Unix

Programming Languages

C/C++, Rust, Java, Python, Javascript, SQL, CSS, HTML

Frameworks

Spring Boot, Hibernate, STL, Boost, Qt6, QML, MLflow

NumPy, Pandas, SciPy, PuLP, Matplotlib, Scikit-Learn, Tensorflow

Parallel and High-Performance Computing

MPI, OpenMP, CUDA

Integrated Development Environments (IDE)

Eclipse, Netbeans, IntelliJ IDEA, Qt Creator, Visual Studio Code, PyCharm

Continuous integration (CI/CD) / DevOps tools

Git, GitLab CI, GitHub, Jenkins, CMake, Maven, Ansible

Software Testing and Quality (TDD, BDD)

SonarQube, Cucumber, CppUnit, Google Test, JUnit, Mockito, Pytest

Microservices and cloud computing

Docker, Kubernetes, Apache Kafka, API REST, GraphQL, AWS, Azure

Databases (SQL and NoSQL)

SQL Server, MySQL, Oracle, PostgreSQL, MongoDB

Scientific Computing /Solvers

MATLAB/ Simulink, GLPK, Gekko, Pyomo, IBM ILOG CPLEX Optimization Studio

Networks, Protocols & Software Standards

CAN, MQTT, TCP/IP, DO-178, ED-109, ED-12C, ARINC 429

Office Tools and Scientific Writing Software

LaTeX, Beamer, Microsoft Word, Excel, PowerPoint, Outlook.

Soft Skills

Autonomy, Leadership

Methodology

Agile Scrum, SAFe, V-Cycle

Education

Training in Data Science and Machine Learning:
-Data analysis and visualization;
-Use of Python Frameworks (NumPy, Pandas, Scikit-Learn, Matplotlib);
-Study of some Machine Learning algorithms: Random Forest, Text Mining, Linear Regression, Decision Trees, K-Means, SVM, Gradient Descent, Neurals Networks.

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A master's degree in Operations Research (M2RO) at ENAC (École Nationale de l'Aviation Civile) is a high-level program specializing in modeling, optimization, operations research and the solving of complex problems related to transportation, aeronautics, logistics, and other industrial sectors.

Modules & courses
Applied Mathematics · Operations Research · Optimization · Mathematical modeling · Linear programming · Combinatorial Optimization · Mathematical Programming · Graphs and networks · Optimization applications in the air transportation · Constraint programming · Optimization under uncertainty · Algorithmic complexity · Air Traffic Management (ATM) · Machine Learning Algorithms

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Languages

English

Advanced

Fluent (Level B2, TOEIC 864: reading, writing and speaking)
French

Expert

Bilingual (Level C2 of the CEFR, TCF 667: Reading, writing and speaking)
German

Notions

Beginner (Level A1 of the CEFR)

Conferences and events

sep. 2021 - déc. 2022 · 1 an -

Référent métier pour ENAC Alumni

(Volunteer)

As a member of the ENAC Alumni Association, I volunteered to support new graduates and students, helping them refine their study or professional plans.

Certifications