Stages, Alternances & Premier Job
Recherchez et affinez pour trouver l'opportunité qui vous convient.
Outils
Info session
GratuitLa méthode pour décrocher des entretiens, en live avec l’équipe.
Recherchez et affinez pour trouver l'opportunité qui vous convient.
Hi Talents • Tunisie
🔥 Nous recrutons ! 🚀 Sales Executive – CDI (100% Remote) 🗓 Démarrage : As soon as possible. Hi Talents est une plateforme carrière qui accompagne les étudiants dans leurs premiers pas professionnels. Notre mission : aider les étudiants à passer du monde académique au monde professionnel grâce à une combinaison unique de technologie, d’IA et d’accompagnement humain. Avec plus de 19000 utilisateurs, nous accélérons notre croissance et recherchons un Sales Executive motivé pour rejoindre l’aventure. Tu veux jouer un rôle clé dans une startup EdTech en pleine croissance ? Tu crois qu’on peut transformer l’accès au monde pro pour les talents grâce à la tech, l’IA et l’accompagnement humain ? Ce job est pour toi. 🧭 Tes missions chez Hi Talents : En tant que Sales, tu seras rattaché(e) directement au CEO et tu feras partie de l'equipe Revenue (Sales & Marketing) Tes missions seront: 🎯 Accompagner nos prospects Freemium vers l’offre Premium 📞 Relancer les prospects existants + Réserver des démos. 🎥 Animer des démos individuelles ou collectives 🎓 Participer à nos événements et jobfairs. 🤝 Collaborer avec le marketing pour optimiser notre tunnel de conversion. 📢 Remonter les feedbacks pour améliorer l’expérience utilisateur. 🧠 Profil recherché : ✅ Tu sais convaincre, inspirer confiance, et gérer les objections ✅ Tu es ultra organisé(e) et orienté(e) résultats ✅ Tu n'as pas peur d'appeler les prospects (une grande partie de ton travail) ✅ Tu es à l’aise à l’écrit, à l’oral, en visio comme en présentiel ✅ Tu vibres pour l’univers startup, l’EdTech et la réussite des jeunes 💼 Ce qu’on t’offre : 🔗 Un CDI en 100 % remote, avec une présence ponctuelle lors de salons et événements. 💰 Une rémunération fixe + variable, directement liée à tes performances et à tes résultats. 🎯 Pas d’horaires fixes : on fonctionne par objectifs. Ce qui compte, c’est l’impact et les résultats. 🚀 Une équipe jeune, ambitieuse et internationale, avec l’envie de construire et d’aller vite. 🌍 Une mission qui a du sens : contribuer concrètement à l’insertion professionnelle et à la carrière de milliers de talents.
CAFPI • Tunisie
Contexte Avec la croissance des activités, la gestion des moyens généraux — ressources, équipements, fournitures et prestations — constitue un levier clé pour le bon fonctionnement de l’entreprise et la maîtrise des coûts. CAFPI Tunisie souhaite simplifier, structurer et automatiser ce processus grâce à des outils de suivi et de pilotage adaptés. Objectifs du PFE Diagnostiquer le processus et les pratiques actuels. Identifier les dysfonctionnements et les tâches répétitives. Optimiser les ressources et maîtriser les dépenses. Simplifier et standardiser les processus. Automatiser les tâches de gestion et de suivi. Construire les KPI et un tableau de bord de pilotage. Recommander des améliorations durables. Résultats attendus Processus : processus formalisé et optimisé ; Outils : suivi et automatisation des tâches de gestion ; Traçabilité : informations plus fiables ; Productivité : réduction des tâches manuelles et répétitives ; Coûts : meilleur suivi des dépenses et des ressources ; Pilotage : tableau de bord d’aide à la décision. Conditions et candidature Lieu : Site CAFPI Tunisie Stage PFE de 6 mois, rémunéré, avec possibilité de pré-embauche. Envoyer un CV en indiquant la référence du sujet.
CAFPI • Tunisie
Objectif du stage Le stagiaire contribue à la mise en place et à l’amélioration des outils de reporting financier. Missions Analyser les besoins en suivi financier des différentes entités et services ; Structurer et automatiser les tableaux de bord financiers ; Optimiser les processus de collecte et de consolidation des données comptables et analytiques ; Améliorer la qualité, la fiabilité et la lisibilité des reportings transmis à la direction ; Proposer des recommandations visant à faciliter la prise de décision et le pilotage de la performance financière. Profil recherché Étudiant(e) en Master Finance ou Analyse financière. Compétences requises Excellente maîtrise d’Excel : formules avancées, tableaux croisés dynamiques, automatisation, etc. ; Très bon niveau en français, à l’écrit comme à l’oral ; Rigueur, esprit d’analyse et sens du détail. Conditions et candidature Lieu : Site CAFPI Tunisie Stage PFE de 6 mois, rémunéré, avec possibilité de pré-embauche. Envoyer un CV en indiquant la référence du sujet.
On sait que chercher une opportunité peut être stressant...
CAFPI • Tunisie
Objectif Optimiser la gestion des tickets IT grâce à l’intelligence artificielle et à l’automatisation des tâches répétitives. Travaux à réaliser Tâches répétitives : identifier et automatiser les actions récurrentes à faible valeur ajoutée afin de réduire les interventions manuelles et les erreurs ; Onboarding automatisé : créer et traiter les tickets selon les dates d’entrée et les processus définis ; Relances automatiques : envoyer des rappels en cas d’injoignabilité des nouveaux collaborateurs, avec notifications aux responsables et aux RH ; Qualification par Copilot : préremplir la catégorie et la priorité, puis appliquer des règles de dispatch vers l’équipe appropriée selon l’entité concernée ; Statistiques par tags : construire des tableaux de bord sur les volumes de tickets, les demandes récurrentes et le suivi des tendances ; Assistance par l’IA : proposer des réponses, aider à la résolution des incidents et améliorer la qualité du traitement. Résultats attendus Réduire les délais de traitement et la charge manuelle ; Fiabiliser les processus ; Améliorer la répartition des tickets ; Renforcer le pilotage de l’activité Support IT. Conditions et candidature Lieu : Site CAFPI Tunisie Stage PFE de 6 mois, rémunéré, avec possibilité de pré-embauche. Envoyer un CV en indiquant la référence du sujet.
CAFPI • Tunisie
Mission Le stagiaire prend en charge la conception, le développement et la mise en production d’une plateforme en quatre volets. Volets du projet 1. Azure Key Vault & sécurité Concevoir le modèle de stockage des accès par banque ; Définir les politiques RBAC ; Gérer les identités avec Entra ID et les identités managées ; Mettre en place la journalisation vers Log Analytics. 2. Application web L’application prévoit deux profils : Un utilisateur pouvant consulter et rechercher ses accès autorisés ; Un administrateur pouvant créer, modifier, faire tourner et supprimer les secrets, ainsi que gérer les droits. L’application utilise le SSO Entra ID. 3. Infrastructure as Code Développer des modules Terraform pour les ressources Azure : Key Vault, hébergement web, réseau et monitoring ; Mettre en place un state distant ; Séparer les environnements. 4. CI/CD avec Azure DevOps Pipelines de validation Terraform : fmt, validate et plan ; Build, tests et analyse de sécurité ; Déploiement multi-environnements avec approbations. Profil recherché Dernière année d’ingénierie ou master en informatique, avec une orientation cloud et sécurité ; Connaissances en cloud, de préférence Azure, et en sécurité des systèmes d’information ; Terraform et Infrastructure as Code ; Développement web : .NET, Node.js, PHP/Symfony ou Python ; API REST ; CI/CD, Azure DevOps et Git ; Rigueur, autonomie et bon français écrit. Planning prévisionnel Mois 1 : analyse de l’existant, cahier des charges et architecture ; Mois 2 : Terraform, Key Vault et pipelines d’infrastructure ; Mois 3-4 : développement de l’application web et des deux profils ; Mois 5 : pipelines applicatifs, tests et recette ; Mois 6 : mise en production, rapport et soutenance. Conditions et candidature Lieu : Site CAFPI Tunisie Stage PFE de 6 mois, rémunéré, avec possibilité de pré-embauche. Envoyer un CV en indiquant la référence du sujet.
CAFPI • Tunisie
Contexte L’Espace Client permet aux clients CAFPI de suivre leur projet de financement et d’échanger avec leur conseiller sur le web et sur mobile. Les technologies utilisées ne sont plus maintenues par leurs éditeurs. Problématique Moderniser l’Espace Client sans perturber le service rendu aux clients, en répondant aux enjeux suivants : Sécuriser les données sensibles sur une base technique qui ne reçoit plus de correctifs ; Simplifier la maintenance, actuellement longue et risquée pour chaque évolution ; Garantir une expérience client simple et fiable sur tous les supports. Objectifs du PFE Analyser l’existant et proposer une architecture cible. Reconstruire l’application, front et back, sur des technologies actuelles et pérennes. Améliorer la sécurité et la qualité de l’application. Simplifier la maintenance et les mises en production. Assurer une migration progressive, sans interruption pour les clients. Conditions Lieu : Site CAFPI Tunisie Stage PFE de 6 mois, rémunéré, avec possibilité de pré-embauche. Candidature Envoyer un CV en indiquant la référence du sujet. Le processus comprend une pré-sélection, un entretien téléphonique, un entretien physique, puis un onboarding personnalisé.
CAFPI • Tunisie
Référence et domaine Sujet 06 — Gestion Lieu : Site CAFPI Tunisie Durée : 6 mois Rémunération : stage rémunéré Perspective : pré-embauche Sujet Optimisation et automatisation du processus des moyens généraux.
CAFPI • Tunisie
Référence et domaine Sujet 05 — Finance Lieu : Site CAFPI Tunisie Durée : 6 mois Rémunération : stage rémunéré Perspective : pré-embauche Sujet Élaboration, fiabilisation et automatisation des reportings financiers.
CAFPI • Tunisie
Référence et domaine Sujet 04 — IT Lieu : Site CAFPI Tunisie Durée : 6 mois Rémunération : stage rémunéré Perspective : pré-embauche Sujet Optimisation de la gestion des tickets IT grâce à l’intelligence artificielle.
CAFPI • Tunisie
Référence et domaine Sujet 02 — IT • Cloud & Sécurité Lieu : Site CAFPI Tunisie Durée : 6 mois Rémunération : stage rémunéré Perspective : pré-embauche Contexte Pour le traitement des dossiers bancaires, notamment la simulation et la demande de prêt, CAFPI télétransmet chaque dossier aux banques partenaires afin d’obtenir leur validation. Cette activité nécessite de nombreux identifiants et mots de passe d’accès à leurs plateformes. L’équipe IT/Cloud administre Microsoft 365 et Azure, maintient les pipelines DevOps et accompagne les équipes. Les accès bancaires doivent être centralisés, tracés et partagés de façon contrôlée. Problématique Comment offrir un point unique et sécurisé pour stocker et retrouver les identifiants des banques utilisés lors de la télétransmission, tout en séparant clairement les droits des utilisateurs de ceux des administrateurs ? Objectifs du projet Centraliser les identifiants des banques dans Azure Key Vault, avec des accès basés sur les rôles (RBAC). Mettre en place une interface web permettant aux utilisateurs de lister les accès bancaires auxquels ils ont droit. Assurer la gestion complète des secrets pour les administrateurs : création, modification, rotation, suppression et gestion des droits. Provisionner toute l’infrastructure en Infrastructure as Code avec Terraform. Automatiser le build, les tests et le déploiement avec des pipelines Azure DevOps. Tracer qui consulte ou modifie quel accès grâce à la journalisation et à l’audit. Missions Le stagiaire prend en charge la conception, le développement et la mise en production de la plateforme, en quatre volets : Azure Key Vault et sécurité Modèle de stockage des accès par banque. Politiques RBAC. Gestion des identités avec Entra ID et les identités managées. Journalisation vers Log Analytics. Application web Profil utilisateur : consultation et recherche des accès autorisés. Profil administrateur : création, modification, rotation et suppression des secrets, ainsi que gestion des droits. Mise en place du SSO avec Entra ID. Infrastructure as Code Création de modules Terraform pour toutes les ressources : Key Vault, hébergement web, réseau et monitoring. Gestion d’un state distant et d’environnements séparés. CI/CD avec Azure DevOps Pipelines de validation Terraform : fmt, validate et plan. Build, tests, analyse de sécurité et déploiement multi-environnements avec approbations. Profil recherché Dernière année d’ingénierie ou master en informatique, avec spécialisation cloud et sécurité. Connaissances en cloud, de préférence Azure, et en sécurité des systèmes d’information. Maîtrise de Terraform et de l’Infrastructure as Code. Compétences en développement web : .NET, Node.js, PHP/Symfony ou Python. Connaissance des API REST. Compétences en CI/CD, notamment Azure DevOps et Git. Rigueur, autonomie et bon français écrit. Planning prévisionnel Mois 1 : étude de l’existant, cahier des charges et architecture. Mois 2 : Terraform, Key Vault et pipelines d’infrastructure. Mois 3 à 4 : développement de l’application web et des deux profils. Mois 5 : pipelines applicatifs, tests et recette. Mois 6 : mise en production, rapport et soutenance.
CAFPI • Tunisie
Référence et domaine Sujet 01 — IT • Développement Lieu : Site CAFPI Tunisie Durée : 6 mois Rémunération : stage rémunéré Perspective : pré-embauche Contexte L’Espace Client permet aux clients CAFPI de suivre leur projet de financement et d’échanger avec leur conseiller, sur le web et sur mobile. Les technologies sur lesquelles l’application repose ne sont plus maintenues par leurs éditeurs. Problématique Comment moderniser l’Espace Client sans perturber le service rendu aux clients ? Les enjeux identifiés sont les suivants : Sécurité : les données sensibles reposent sur une base technique qui ne reçoit plus de correctifs. Maintenance : chaque évolution est longue et risquée. Expérience client : garantir un parcours simple et fiable sur tous les supports. Objectifs du PFE Analyser l’existant et proposer une architecture cible. Reconstruire l’application, front et back, sur des technologies actuelles et pérennes. Améliorer la sécurité et la qualité de l’application. Simplifier la maintenance et les mises en production. Assurer une migration progressive, sans interruption pour les clients.
ReDX Technologies • Tunisie
Project overview This 6-month PFE project builds a simulation and prediction pipeline for laser cutting of a steel casing backed by cement during well plug and abandonment (P&A). Lasers provide a contactless alternative to mechanical milling, but engineers need a rapid way to estimate cutting time and the heat reaching the cement behind the casing. The project will develop a 2D axisymmetric finite-element thermal model of a laser heating a casing-cement section, remove material once it reaches melting temperature, add a basic thermal-stress check and use a set of simulations to train a regression model predicting cutting time and cement heating in seconds instead of running new simulations. Project goals Build a 2D axisymmetric transient thermal model in Ansys of a Gaussian laser heat source applied to a steel casing with a cement layer behind it, using constant material properties and element removal at melting temperature. Validate the model against published laser-cutting data, then study the effects of laser power, beam radius and casing thickness on cutting time and cement temperature. Add a one-way thermal-stress check on the casing and cement. Run approximately 150–200 simulations and generate a database for a regression model predicting cutting time and peak cement temperature from laser settings. Required skills Must-have Finite-element analysis and heat-transfer basics. Hands-on experience with Ansys. Python for scripting and data analysis. Nice-to-have Laser processing or well-construction/P&A knowledge. Basic machine learning, including scikit-learn. Deliverables FE thermal model of the casing-cement section with documented material properties and assumptions. Parameter-study report showing how laser settings affect cutting time and cement heating. Trained prediction model with accuracy checked against held-out simulations. Final report and repository with instructions to rerun and extend the model. Suggested phased plan Month 1: Literature review, material properties, model geometry and mesh. Month 2: Transient thermal model with laser heat source and element removal. Month 3: Validation against published data and thermal-stress check. Month 4: Parameter study and simulation sweep. Month 5: Prediction-model training and testing. Month 6: Final report and handover. Conditions Recommended period: 6 months, full-time PFE. Monthly stipend with potential end-of-internship performance bonus. Potential paper-publication co-authorship.
ReDX Technologies • Tunisie
Project description This six-month project develops a machine-learning surrogate model for automotive aeroacoustics using a provided dataset. The model will rapidly predict wind-noise quantities generated by exterior parts, initially focusing on the side-mirror and A-pillar region, including overall sound pressure level, noise spectra and side-glass pressure-fluctuation levels. The surrogate is intended to provide near-instant evaluation compared with transient CFD and acoustic simulations. The project then extends to predicting acoustic impacts of part modifications and couples the acoustic surrogate with an existing drag-coefficient surrogate for noise-aware multi-objective design optimization. Goals Preprocess the dataset, select a geometry representation and train a model predicting OASPL and noise spectra on held-out geometries using a leakage-free protocol. Evaluate MAE in dB, RMSE and correlation. Add uncertainty estimation and failure-mode analysis. Assess prediction of noise differences between baseline and modified parts, such as side-mirror variants. Attribute which regions drive predicted noise. Couple the acoustic and Cd surrogates to obtain a drag-versus-noise Pareto front and create a quiet-design gallery. Required skills Must-have: Python and ML fundamentals using PyTorch or TensorFlow; basic external-aerodynamics knowledge; acoustics basics including sound pressure level, dB, spectra/PSD and frequency bands; data preprocessing and experiment tracking; and ability to work with large datasets, I/O, storage and batching. Nice-to-have: 3D learning, signal processing, aeroacoustics, multi-objective or Bayesian optimization, uncertainty and calibration, geometry-processing tools such as Open3D, trimesh, PyVista or meshio, and HPC/GPU training workflows. Deliverables and schedule Deliverables include an acoustic-surrogate package with preprocessing scripts, checkpoints, inference code and benchmarks; a part-level analysis module with uncertainty estimates, ΔdB predictions and attribution maps; a coupled Cd-plus-noise optimization loop with Pareto front and quiet-design gallery; and a clean reproducible repository, README, handover instructions and final report. The suggested plan covers literature review and preprocessing in month 1, baseline OASPL modeling in month 2, model improvement and spectral prediction in month 3, part-swap and ΔdB analysis in month 4, optimization and attribution in month 5, and packaging, benchmarking, gallery and handover in month 6. Monthly stipend with potential end-of-internship performance bonus and potential paper-publication co-authorship.
ReDX Technologies • Tunisie
Project description This six-month PFE develops a drone-based vehicle-surveillance pipeline covering detection, tracking, restoration and information extraction such as license plate, brand and color. It targets long-range/high-altitude operation and night or low-visibility conditions. At long range, vehicles and license plates occupy only a few pixels, while turbulence, haze and jitter degrade images. At night or in fog, an RGB camera becomes unreliable, so a thermal infrared camera is added. Thermal imagery introduces limitations including lower resolution, lack of color, unreadable plates and RGB/thermal misalignment. Goals Develop long-range perception for vehicle detection, tracking and identification under motion blur and atmospheric turbulence, using multi-frame super-resolution optimized for recognition and abstention when evidence is insufficient. Use RGB-thermal fusion to track vehicles day and night, reading plates from RGB whenever possible. Measure performance by distance and lighting and create an operational envelope recommending actions such as descending, zooming or switching sensors when identification is unreliable. Work to be performed Assemble paired RGB-thermal data from public drone datasets labeled by altitude, angle and lighting. Train and compare detectors, trackers and super-resolution models across distance bands. Test robustness to blur and turbulence. Build and compare RGB-only, thermal-only and fused systems. Build the operational-envelope model and integrate the complete pipeline into a live-overlay demonstration. Required skills Must-have: strong PyTorch skills, clean ML engineering practices, computer-vision experience in detection, tracking and image restoration, and understanding of camera geometry and multi-camera calibration. Nice-to-have: thermal infrared imaging, RGB-thermal fusion, super-resolution, small-object detection and edge deployment using TensorRT or quantization. Deliverables and schedule Deliverables include a long-range perception module, RGB-thermal fusion module, benchmark and operational-envelope model, integrated live-overlay demo, technical report and reproducible repository. Months 1–2 cover data assembly, detection, tracking and distance estimation; months 3–4 cover super-resolution and RGB-thermal fusion; months 5–6 cover the operational envelope, integration, final report and handover. Monthly stipend with potential end-of-internship performance bonus and potential paper-publication co-authorship.
ReDX Technologies • Tunisie
Project overview A new HPC storage infrastructure must be designed before its applications are deployed. Users can estimate capacity, budget and reliability but often cannot describe future input/output behavior. This internship extends an existing Lustre architecture recommender with patterns learned from historical HPC I/O logs. The project will discover representative workload profiles, match them to simple user descriptions and use them to refine feasible storage recommendations without requiring application code or logs from users. ReDX has developed a Lustre storage recommender that collects numeric requirements, preferences and constraints through a conversation, then performs sizing, hardware selection and physical architecture validation. The current system does not yet use patterns learned from real application I/O behavior. Public Darshan logs provide historical I/O statistics. If profiling access is approved on Toubkal, pre-written workload descriptions may be paired with measured I/O behavior. Project goals Historical I/O analysis and workload profiling Collect and analyze public logs to study read/write activity, access sizes and other recorded I/O characteristics. Use data analysis and simple clustering methods to identify representative workload profiles and document their variability. Learning from application descriptions Where permissions and tools allow, create paired examples on Toubkal by writing a short description before executing a workload and associating it with its measured I/O profile. Start with expert-reviewed rules and a simple model such as logistic regression. Evaluate the approach on unseen applications. Consider more advanced language models only if they demonstrate an improvement. If reliable paired examples are unavailable, use transparent scenario matching and report uncertainty rather than claiming supervised prediction. Integration and evaluation Reuse the existing requirement and preference extractors and add optional questions about expected application usage. Use plausible workload profiles to compare feasible Lustre architectures without changing mandatory capacity, budget, compatibility or reliability constraints. Evaluate profile quality, classification performance and generalization to unseen applications. Compare recommendations with and without workload information and check feasibility and uncertainty. Support performance claims with appropriate measurements. Required skills Good Python programming, basic Linux skills and familiarity with Git. Basic knowledge of data analysis or machine learning. Willingness to learn HPC storage concepts. Good analytical skills, technical English and interest in experimental research. Training Introduction to HPC storage, Lustre and Darshan I/O profiling. Guided training in data preparation, basic machine learning and research evaluation. Regular mentoring and technical reviews with ReDX engineers and research supervisors. Deliverables Reproducible I/O dataset preparation scripts and documented workload-profile library. Tested description-to-profile matcher integrated into the existing Lustre recommender. Comparative experiments, source code, documentation and final PFE report. Conditions Recommended period: 4 to 6 months. Collaborative research toward a joint journal paper with ReDX and academic supervisors. The student may contribute to experiments and manuscript preparation; publication and authorship depend on results and contributions. Opportunity to work with real HPC I/O data and, subject to authorization, conduct profiling experiments on Toubkal.
ReDX Technologies • Tunisie
Project description This internship focuses on building tools to automate routine tasks, organize data and monitor daily operations. The project covers workflow automation, data management, monitoring and reporting, with the goal of reducing manual work, improving information management and helping teams identify and resolve issues. Objectives AI workflow automation and tracking Identify repetitive tasks and automate routine checks, notifications and reports. Track changes to prompts, models and settings, including the versions used for each AI result. Record inputs, outputs, evaluation scores, processing times and errors. Run repeatable tests to evaluate the effect of changes on AI results. Data management Create consistent data-organization methods. Define rules for storing, checking, updating and retaining information. Identify missing, incorrect and duplicate data. Track data provenance and changes. Prepare reliable datasets for analysis and future use. Monitoring and reporting Centralize logs and activity information. Track service availability, processing times and errors. Build dashboards and alerts for service interruptions, repeated failures, slow processing and growing queues. Generate reports showing performance trends and recurring issues. Integration and testing Connect the developed tools to existing systems. Check workflow execution and failure handling. Link AI results to the versions and settings used. Evaluate data quality, performance and monitoring. Measure time saved and reductions in manual work. Required skills Python, SQL, APIs and backend development; basic understanding of AI workflows and data management; interest in automation, testing and monitoring; problem-solving and English communication skills; and familiarity with Git. Experience with automation platforms, dashboards, Databricks or MLflow is a plus. Deliverables and conditions Deliverables include automation, AI-tracking, data-management and monitoring tools; dashboards, alerts and automated reports; source code and maintenance instructions; a technical report; and a demonstration. Recommended duration: 4–6 months. Monthly stipend with possible end-of-internship performance bonus. Opportunity to work with end customers.
ReDX Technologies • Tunisie
Project overview This internship project focuses on designing and implementing secure AI-based processing of multimodal submissions, with controlled handling of user data before and during execution of an AI pipeline. The objective is to improve the privacy and security of AI model processing on the REDX platform while ensuring that sensitive content is handled safely throughout the pipeline and AI decision workflow with minimum loss of information. The platform already processes user interactions, operational outcomes and AI-assisted processing results. As these flows evolve, stronger privacy protection and secure operation are required. Project goals Design privacy-preserving, secure and encrypted AI inference workflows for the REDX platform. Investigate Trusted Execution Environments (TEE) and confidential computing. Assess the feasibility and performance trade-offs of Secure Multi-Party Computation (MPC) and split inference for multimodal workloads. Investigate the performance, security level and latency of the developed solution. Apply execution-acceleration techniques to reduce end-to-end inference time. Protect AI decisions against prompt injection and manipulation attempts to ensure reliable and trustworthy inference. Required skills Knowledge of data engineering and data quality, with experience in Python and backend integration. Understanding of AI/ML, multimodal models and inference pipelines. Awareness of security, privacy-preserving techniques and access control. Analytical skills and English communication skills. Familiarity with Git. Deliverables Technical report detailing the platform design, implementation and evaluation. Source code and documentation for the secure AI workflow-processing solution and prompt-injection prevention mechanism. Performance metrics covering data quality, AI inference performance, security and safeguarding efficiency. Demonstration prototype showcasing secure AI processing. Conditions Recommended period: 4 to 6 months. Monthly stipend, with a possible end-of-internship performance bonus. Possibility to work with end customers.
ReDX Technologies • Tunisie
Context and objectives This PFE project focuses on deploying and validating a comprehensive scientific and AI software ecosystem on an HPC cluster. The stack includes programming compilers, parallel programming libraries, mathematical libraries, GPU computing frameworks, scientific applications and AI workloads. The project will establish reproducible deployment and validation procedures to support future software-stack maintenance and evolution. Project goals Deploy the latest stable versions of compiler suites and toolchains, including GCC and Intel oneAPI. Install and validate MPI, OpenMP, BLAS/LAPACK, FFTW, HDF5 and related mathematical and scientific libraries. Deploy and validate CUDA, cuDNN and relevant GPU-accelerated libraries and frameworks. Install and validate applications such as GROMACS, LAMMPS, CP2K, VASP and Quantum ESPRESSO. Deploy and validate PyTorch, TensorFlow and associated GPU-acceleration components. Develop and maintain reproducible EasyBuild recipes and Lmod modulefiles. Perform functional and performance validation across CPU and GPU resources. Establish automated deployment, testing and validation procedures. Scope and methodology The project covers: Compiler and development toolchains, with support for C, C++ and Fortran applications. MPI implementations including OpenMPI and Intel MPI, OpenMP, BLAS/LAPACK, optimized mathematical libraries, FFTW, HDF5, NetCDF, cuBLAS, cuFFT and cuDNN. Scientific and engineering applications including GROMACS, LAMMPS, CP2K, Quantum ESPRESSO, VASP and ParaView. AI and machine-learning frameworks including PyTorch, TensorFlow, CUDA-enabled dependencies, NumPy, SciPy and scikit-learn. EasyBuild recipes, Lmod modulefiles and automated validation tests. Validation will cover installation and dependency resolution, compiler and MPI compatibility, CPU and GPU functionality, single-node and multi-node execution, reference performance benchmarks and reproducibility across software versions and deployment environments. Representative benchmarks include HPL, OSU Micro-Benchmarks, STREAM and application-specific test cases. Deliverables Comprehensive documented HPC software stack covering compilers, libraries, scientific applications and AI frameworks. Reproducible EasyBuild recipes and associated Lmod modulefiles. Validation reports covering installation status, compatibility, functionality and performance. Software compatibility matrix covering compiler versions, MPI implementations, libraries and application dependencies. Automated deployment and validation scripts. Technical documentation describing deployment procedures, configurations, limitations and maintenance recommendations. Required skills Strong problem-solving skills, curiosity and willingness to learn new technologies. Familiarity with Linux environments and command-line operations. Programming skills in C/C++ or Python. Basic understanding of software compilation, libraries and dependency management. Familiarity with Git and version control. Interest in HPC, scientific computing or AI. Good command of English. Ability to work independently and collaboratively. Training and conditions Linux fundamentals through the “Complete Linux Training Course” on Udemy. Introduction to HPC systems and parallel programming, including MPI, OpenMP and GPU computing fundamentals. Hands-on training on EasyBuild and Lmod. Introduction to software compilation, toolchains, dependency management and environment modules. Practical training on deploying and benchmarking scientific and AI applications. Introduction to automated testing and validation using scripting and benchmarking tools. Mentorship through 1:1 sessions with ReDX engineers and HPC domain experts. Recommended period: 6 months. Monthly stipend, with the possibility of an end-of-internship performance bonus. Possibility to work with end customers. Hands-on experience with a production HPC environment and a broad portfolio of scientific and AI applications.
ReDX Technologies • Tunisie
Context and objectives This PFE project analyzes how software deployment strategies and filesystem placement affect the performance of scientific Python workloads on High-Performance Computing (HPC) clusters. Scientific environments managed with Conda, EasyBuild and Singularity/Apptainer may contain thousands of small files. When deployed on shared filesystems such as Lustre or NFS, simultaneous initialization across multiple compute nodes can generate substantial metadata activity, filesystem contention and startup latency. The project will deploy equivalent PyTorch environments using Conda, EasyBuild and Apptainer across NFS, Lustre and node-local storage. Project goals Evaluate the impact of scientific Python environment placement on shared-filesystem metadata activity and application startup performance. Investigate Singularity/Apptainer containers as a way to reduce filesystem metadata overhead. Assess the benefits and costs of staging software environments and container images on node-local storage. Characterize the influence of deployment strategies on PyTorch initialization, GPU training throughput and multi-node scalability. Experimental work Measure installation time, Python library import latency, CUDA initialization and metadata-related operations. Execute PyTorch benchmarks, including ResNet-50 and DistributedDataParallel (DDP), using different deployment configurations and process-concurrency levels. Analyze startup time, GPU utilization, training throughput and multi-node scalability. Evaluate packaging environments into Apptainer images and staging them onto node-local storage. Develop a lightweight framework to automate experiments and recommend deployment strategies according to workload characteristics and measured overheads. Distinguish environment-related filesystem activity from training-dataset I/O while accounting for staging costs and filesystem caching effects. Deliverables Reproducible benchmarking framework for Conda and containerized PyTorch environments. Comparative performance analysis across Lustre, NFS and node-local storage. Evaluation of filesystem metadata overhead and its impact on large-scale Python application initialization. Practical guidelines for efficient scientific Python environment deployment on HPC systems. Required skills Strong problem-solving skills, curiosity and willingness to learn new technologies. Familiarity with Linux environments. Programming skills in C/C++ or Python. Familiarity with Git and version control. Good command of English. Ability to work independently and collaboratively. Training and conditions Linux fundamentals through the “Complete Linux Training Course” on Udemy. Introduction to HPC systems and parallel programming, including MPI, OpenMP and GPU basics. Training on HPC software management tools such as EasyBuild. Mentorship through 1:1 sessions with ReDX engineers and domain experts. Recommended period: 6 months. Monthly stipend, with the possibility of an end-of-internship performance bonus. Possibility to work with end customers.
Four Seasons Tunisia • Tunisie
About Four Seasons Four Seasons is powered by our people. We are a collective of individuals who strive to become better, push ourselves to new heights, and treat each other as we wish to be treated. Our team members around the world create amazing experiences for our guests, residents, and partners through a commitment to luxury with genuine heart. We know that the best way to enable our people to deliver exceptional guest experiences is through a world-class employee experience and company culture. At Four Seasons, we believe in recognizing a familiar face, welcoming a new one, and treating everyone we meet the way we would want to be treated ourselves. Whether you work with us, stay with us, live with us, or discover with us, our purpose is to create impressions that will stay with you for a lifetime. This comes from our belief that life is richer when we truly connect with the people and the world around us. About the Location Beachfront relaxation on North Africa’s Mediterranean coast. In the heart of Tunisia’s vibrant and exotic capital city, our modern Mediterranean oasis offers a calm, laid-back beachfront escape alongside this bustling business hub. Relax at our spa and pools, stay connected with state-of-the-art business services, or take a step back in time—more than 3,000 years of ancient civilization are at your fingertips. About the Role The Digital Marketing & Public Relations Intern supports the execution of the hotel’s digital marketing, communications, and public relations initiatives in alignment with Four Seasons brand standards. What You Will Do Manage and schedule content across the hotel’s social media platforms. Monitor social media engagement and support community management. Maintain and update social media content calendars and campaign timelines. Create engaging photo, video, and written content for digital channels. Support SEO initiatives by optimizing website content and monitoring keyword performance. Assist with photo shoots, video production, and digital asset management. Monitor social media, hospitality, and competitor trends to identify content opportunities. Support influencer, content creator, and media collaborations. Assist with public relations activities, including media outreach, press kits, and media monitoring. Track campaign performance, online reviews, and guest feedback while preparing marketing and PR reports. What You Bring Final-year student seeking a six-month Graduation Project Internship (PFE) in Marketing, Communications, Public Relations, Digital Media, Hospitality, or a related field. Strong interest in digital marketing, social media, content creation, SEO, communications, and public relations. Excellent written and verbal communication skills in English and French. Familiarity with major social media platforms and basic knowledge of social media management and analytics tools such as Meta Business Suite, Sprout Social, or similar platforms. Familiarity with SEO principles, website content management, and digital marketing best practices is an asset. Basic knowledge of content creation tools such as Canva, Adobe Creative Suite, CapCut, or similar platforms. Analytical mindset with an interest in campaign performance tracking and reporting. Attention to detail, creativity, and a proactive approach to learning. Comfortable working with Microsoft Office applications, particularly PowerPoint and Excel. Passion for storytelling, luxury hospitality, travel, lifestyle, and creating engaging digital experiences. What We Offer Hands-on experience in a luxury hospitality environment while supporting real marketing and public relations initiatives. Mentorship from experienced Marketing, Communications, and Public Relations professionals. Opportunities to collaborate with multiple departments and gain a comprehensive understanding of luxury hotel operations. Complimentary employee meals. Paid internship. Schedule & Hours Full-time
Recherchez et affinez pour trouver l'opportunité qui vous convient.