Stages & Alternances

Project_ID20 : Development of a Multi-Board Data Acquisition Solution Utilizing STM32 MCU using ethernet/PTP protocol for customer application PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Overview and Purpose Create a highly accurate and synchronized multi-board data acquisition platform based on STM32 MCUs and Precision Time Protocol (PTP). Enable customers to collect and analyze time-critical, distributed data with precision and reliability across multiple STM32 boards. Main Tasks and Technical Objectives Develop a solution enabling coordinated data acquisition from multiple STM32 boards with accurate calculation and compensation for network latency. Integrate PTP for precise time synchronization across multiple boards and ensure deterministic timestamping across the system. Deliverables Provide functional example code (firmware) demonstrating multi-board acquisition, PTP synchronization, and latency compensation strategies. Deliver detailed documentation and support materials (design notes, API usage, integration guide, test procedures) to help the TOMAS team address advanced Ethernet synchronization and multi-board coordination questions. Required Skills, Tools and Technologies Embedded C development experience using toolchains such as IAR and STM32CubeMX; familiarity with HAL drivers and relevant middleware. Knowledge of Ethernet networking, PTP (IEEE 1588), time synchronization techniques, and compensation algorithms for network-induced latency. Expected Activities and Tests Implement and test synchronization accuracy across multiple STM32 nodes using PTP and measure end-to-end timestamp fidelity. Implement latency measurement and compensation methods; produce reproducible benchmark results and validation procedures. Project Context and Environment PFE Book STTunis 2026 — work with the TOMAS team at STMicroelectronics (Tunis) on a customer-facing application. Keywords: Embedded C, IAR, STM32CubeMX, HAL drivers, Middleware, PTP, Ethernet.

18 AI Assistant for Electronic Component Database PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Project Summary Develop and refine an AI Document Interpreter capable of reading, summarizing, and extracting key information from technical documents (datasheets, application notes, etc.). Implement a Component Adviser that provides component insights, propositions from the component database, and quick technical guidance for component selection. Build a Dashboard Database Generator to create statistics and dashboards for visualizing component attributes and trends. Objectives & Purpose Elevate the component database user experience by integrating an intelligent engineering AI assistant that merges document understanding, recommendation, and visualization. Transform traditional component data into actionable intelligence to support faster and more informed design decisions. Main Tasks / Work To Be Done Design and implement the AI Document Interpreter pipeline: ingestion, parsing, semantic extraction, and summarization for datasheets and application notes. Create the Component Adviser module to query the database, rank candidate components, and produce concise technical guidance for selection. Develop the Dashboard Database Generator to compute statistics, generate visual dashboards, and expose APIs or UI components for visualization. Technical Stack & Keywords Expected technologies: Python, Generative AI (large language models / document understanding), data processing and ETL for component metadata. Domain focus: electronic engineering tools, electronic components, component databases, metadata extraction and analytics. Expected Deliverables & Outcomes A working AI Document Interpreter capable of accurate extraction and summarization for typical technical documents used in component selection. A Component Adviser prototype integrated with the component database that returns ranked suggestions and short technical rationale. Dashboards and generated statistics showcasing component attributes, usage metrics, and comparison views to support engineer decisions. Additional Notes The project is part of the PFE Book STTunis 2026 offerings and targets improving engineering workflows around electronic component selection and analysis. Emphasis on combining AI-driven document understanding with real-time recommendations and automated dashboard generation to make data actionable.

Project_ID19 Application Note on how to use PMU and ETR to debug and trace STM32 applications PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Project overview Study and document the STM32 Debug and Trace infrastructure, with emphasis on the new Debug and Trace unit. Produce an application note that explains PMU (Performance Monitoring Unit) and ETR (Embedded Trace Router), their principles of operation and practical usage. Work to be done / Objectives Define and explain PMU and ETR, including working principles and architecture-level considerations for Arm Cortex-M / STM32 CPUs. Identify and document concrete use cases and decision guidelines on when to use PMU vs ETR for performance analysis, debugging and trace. Technical tasks & deliverables Develop step-by-step practical examples demonstrating how to use PMU and ETR to debug and trace applications on STM32, providing instructions for both IAR and Keil IDEs. Deliver a complete application note (document) containing theory, configuration steps, example projects, measured results and recommended workflows for engineers. Required skills & environment Strong knowledge of Embedded C and firmware development for STM32 (Arm Cortex-M CPU). Experience with debugging and trace tools; familiarity with IAR Embedded Workbench and Keil MDK is required. Familiarity with STM32 architecture, performance counters (PMU) and trace streaming/router concepts (ETR) is a plus. Keywords & context Embedded C, IAR, KEIL, STM32 architecture, Arm Cortex-M CPU, STM32 debugging. The work targets engineers and firmware developers who need reproducible, IDE-specific procedures to enable PMU/ETR-based debugging and performance analysis. How to apply Apply online using the provided link. The job posting contains full application details and recruitment information. Link to apply: https://stmicroelectronics.eightfold.ai/careers?location=Tunis%2C%20Tunisia&pid=563637157282200&domain=stmicroelectronics.com&sort\_by=relevance&hl=en&triggerGoButton=false&triggerGoButton=true

Project_ID17 Maintenance and lifecycle management of a STM32 MCU & MPU Evaluation Tool PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Purpose / Objectives Ensure long-term reliability and availability of STM32 MCU and MPU evaluation tools through proactive component obsolescence management. Provide hands-on experience in hardware maintenance and industrialization processes, exposing the trainee to real-world product lifecycle challenges. Main Responsibilities / Work To Be Done Monitor and manage obsolescence alerts for electronic components and identify suitable replacements (footprint, electrical compatibility, sourcing). Redesign related promotion/evaluation boards ensuring compliance with design standards and prepare updated manufacturing files (Gerbers, BOM, assembly drawings). Update production test packages to reflect hardware revisions and synchronize test procedures with new board revisions. Revise and publish all technical documentation across internal and external platforms to reflect hardware changes and ensure traceability. Drive the management and synchronization of critical project milestones from kick-off to product release, coordinating with stakeholders to meet release targets. Required Skills & Tools / Technical Keywords STM32 MCUs and MPUs experience and general electronics knowledge (component selection, PCB design considerations). Altium Designer for schematic/PCB redesign, generation of manufacturing files and BOM management. Embedded C for firmware understanding; Python and VBA for automation of scripts and data processing. Version control and documentation tools: Subversion (TortoiseSVN), Microsoft Office (Word, Excel) and documentation publishing workflows. Expected Deliverables / Outcomes Updated/evaluated component list with documented replacements and risk assessment for obsolescence. Redesigned promotion/evaluation boards with complete manufacturing files and updated BOMs. Revised production test packages and validated test procedures for the revised hardware. Published and synchronized technical documentation across internal and external platforms; clear project milestone tracking until release. Additional Information Keywords provided: STM32 MCUs, Altium Designer, Embedded C, Python, VBA, Subversion (TortoiseSVN), Microsoft Office (Word, Excel), Projects Management, Electronics knowledge. Reference: PFE Book STTunis 2026 — Project\_ID17.

Project_ID16 Development of a Cross-Platform Application Manager for ST Software Tools PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Purpose Develop a cross-platform desktop application to list, install, uninstall, update and launch a set of ST applications. Centralize the management of ST software tools across Windows, Linux and Mac to provide a unified user experience. Work to be done / Responsibilities Design and develop a user-friendly interface for listing applications and displaying their installation / update / launch status. Implement features to launch, install, uninstall and update each application with reliable state handling and clear feedback to the user. Ensure cross-platform compatibility and behaviour parity across Windows, Linux and Mac. Technical requirements & Keywords Expected technologies: Electron, TypeScript (desktop cross-platform framework and language indicated by keywords). Domain knowledge: STM32Cube ecosystem and familiarity with distribution/installation patterns for embedded development tools is an advantage. Target platforms: Windows, Linux, Mac — ensure packaging, auto-update and installer/uninstaller flows for each platform. Deliverables / Expected outcomes A working cross-platform desktop application that can list available ST tools, show their status, and perform install/update/uninstall/launch operations. Documentation on build, packaging and platform-specific installation/uninstallation procedures; test cases validating cross-platform behaviours. How to apply Online application (see link below). Link to apply: https://stmicroelectronics.eightfold.ai/careers?location=Tunis%2C%20Tunisia&pid=563637157282200&domain=stmicroelectronics.com&sort\_by=relevance&hl=en&triggerGoButton=false&triggerGoButton=true

Project_ID15 STM32CubeProgrammer incremental flashing enhancement PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Project overview This internship (PFE) aims to study and develop advanced incremental programming solutions for the STM32CubeProgrammer tool. The trainee will benchmark existing incremental programming solutions and compare them with the current ST solution to identify gaps and improvement opportunities. Objectives & main tasks Define an enhancement strategy for the ST incremental programming solution based on the benchmarking results. Design and implement the technical solution to bring STM32CubeProgrammer to the level of the most advanced incremental programming implementations. Develop a graphical interface (JavaFX) supporting incremental programming in addition to improving the existing CLI solution. Enhance and extend the automatic test suite to cover the new/integrated incremental programming behavior. Technical environment & keywords Technologies and platforms: STM32CubeProgrammer, STM32 microcontrollers, STLink, C++ and JavaFX for GUI development. Focus areas: incremental flashing algorithms, firmware update mechanisms, CLI and GUI integration, automated testing for flashing workflows. Expected deliverables A benchmark report comparing existing incremental programming solutions with ST's current implementation, including proposed enhancement strategy. Implemented enhancements in STM32CubeProgrammer (code changes, algorithms) and a working JavaFX graphical interface that supports incremental flashing. Updated automatic test suite covering the developed features and validation reports demonstrating reliability and performance improvements. Context & supervision Project listed as PFE Book STTunis 2026, identified as Project\_ID15. The work involves close collaboration with ST teams responsible for STM32CubeProgrammer and firmware flashing/tooling experts. Skills & candidate profile Strong C++ development skills; experience with Java/JavaFX for GUI development is required or highly desirable. Familiarity with embedded systems (STM32), STLink, firmware flashing/update procedures and automated testing frameworks is expected. Notes The project includes both CLI and GUI development and requires extending automated tests to validate incremental programming behavior. Link to apply is provided separately.

Project_ID13 Automation of Security System Validation Bootpath Lifecycle PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Purpose and Context This internship project aims to study and develop advanced applicative use cases for Security System validation activity, covering the entire BootPath lifecycle from project configuration, project build to project application execution on STM32 board. The work focuses on defining complex use cases for BootPath lifecycle usage from the End of User and on validating the complete Cube ecosystem (STM32CubeMX, STM32CubeIDE, STM32CubeProgrammer and the STM32CubeFW). Main Tasks and Deliverables Define complex use cases for BootPath lifecycle usage that exercise the whole STM32 Cube toolchain (project configuration, build, flashing, and execution on target board). Design and develop an application implementing these complex use cases and integrate this application with the existing automatic Validation platform. Technologies, Tools and Methods Work will involve STM32CubeMX, STM32CubeIDE, STM32CubeProgrammer, STM32CubeFW and STM32xx MCU architecture knowledge. Test automation and scripting tools expected to be used include Python (PyWinAuto), Robot Framework, UFT and Git for version control. Expected Skills and Profile Good understanding of embedded systems and STM32 microcontroller ecosystem (Cube tools and firmware). Experience or strong interest in test automation, scripting (Python) and integrating applications with validation/CI platforms. Integration and Validation Scope Integrate the implemented application with the entire Validation automatic platform so that the defined use cases are executed and validated end-to-end on STM32 boards. Validate interactions across the toolchain: configuration (CubeMX), IDE build (CubeIDE), programmer (CubeProgrammer) and runtime behavior with STM32CubeFW. Miscellaneous Project reference: PFE Book STTunis 2026 — Project\_ID13. Number of trainees needed is indicated in the original posting (see application link).

Project_ID12 : STM32 Digital Characterization : New Tester and Board bench for Teradyne PFE

STMicroelectronics •

Hybride3-6 moisExpire dans 14 jours

Purpose Develop a new test platform and characterization board for STM32 using the Teradyne J750 tester. Improve digital IP measurement accuracy by designing dedicated hardware and software, and validating results through experimental correlation with existing database data. Work to be done / Main Tasks Acquire technical knowledge of the Teradyne J750 HW/SW ecosystem through documentation study and setup of the test environment. Design and develop a PCB characterization board for STM32 devices and integrate it with the Teradyne J750 bench. Architect and implement software for the new testing methodology to automate measurements and data collection. Perform measurements on STM32 devices, correlate measured data with the database, and validate and document results for reliable characterization. Required skills & tools Embedded C programming and experience with STM32 MCUs. Debugging and instrumentation skills (oscilloscope, instrumentation tools) and experience with PCB design tools such as Altium and signal integrity tools such as HyperLynx. Experience or knowledge of MS Visual Basic and Python for test software and data correlation; optional experience with LabVIEW and AI tools is a plus. Deliverables & Outcomes A functioning PCB characterization board for STM32 validated on the Teradyne J750 platform. Test software implementing the new testing methodology and scripts/tools for automated measurements and data correlation. Measurement report comparing experimental results with database references, including validation and recommendations for IP characterization accuracy. Context & Additional information PFE Book STTunis 2026 — project based on Teradyne J750 test environment and STM32 platforms. Keywords provided by the host: Embedded C, STM32 MCUs, Oscilloscope/instrumentation, Altium, Hyperlynx, MS Visual Basic, Python; optional: LabVIEW and AI tools.

Project_ID14 Development of a Framework for Testing the Performance and Robustness of the MX2 CLI/CoopAPI PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Purpose The objective of this project is to design and implement an intelligent automated framework to evaluate the performance and robustness of the MX2 Command Lines Interface (CLI) and Cooperation API. Additionally, the framework will assess the impact of the CLI/Cooperation API on the MX2 GUI interface responsiveness and stability. Work to be done / Tasks Automate the execution of MX2 CLI commands and Cooperation API calls according to defined test scenarios and command sequences. Inject various parameters to introduce test variations and accurately record the execution time of each CLI command and Cooperation API call. Perform measurement and comparison by collecting real-time performance metrics such as CLI/API execution time per command. Evaluate the influence of CLI/API operations on the MX2 GUI interface responsiveness and stability. Technical environment & Keywords Recommended technologies and tools: Python, RobotFramework, Psutil Library, JSON, SQLite, HTML, Selenium, Jenkins. Target data storage and reporting: store measured metrics (e.g., execution times) in SQLite/JSON and produce HTML reports; integrate test runs with Jenkins for automation. Expected deliverables A reusable automated test framework capable of executing CLI and API sequences, injecting parameter variations, and collecting timing and performance metrics. Scripts/tests (RobotFramework/Python), sample scenarios, and generated performance reports demonstrating measurement and comparison results and GUI impact analysis. Evaluation criteria Correctness and reliability of automated execution (CLI & API) across defined scenarios. Accuracy of timing measurements and quality of performance metrics and comparisons. Assessment of GUI responsiveness and stability under CLI/API load and clarity of resulting reports for stakeholders.

Project_ID11 STM32 RNG Characterization for NIST Certification PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Purpose The project focuses on the characterization and validation of the STM32 Random Number Generator (RNG) Entropy to ensure high-quality randomness. It aims to support compliance with the NIST SP800-90b certification standards for cryptographic security. Work To Be Done / Tasks Setup a process to extract continuous random bits from the STM32 RNG and run statistical tests to evaluate randomness quality. Perform Entropy Source Validation (ESV) on STM32 RNG and document results and methods. Support NIST SP800-90b certification documentation and update existing application notes. Update AN4230 to include latest RNG results and methods, integrating findings into clear technical guidance. Required Skills & Tools Experience with STM32 and Embedded C for accessing and extracting RNG output from microcontrollers. Knowledge of RNG Entropy Source Validation, embedded security, and basic cryptography concepts. Familiarity with Python and OpenSSL is optional but useful for statistical testing and post-processing of entropy data. Deliverables & Expected Outcomes A validated process for continuous random bit extraction and a set of statistical test results demonstrating entropy quality. Comprehensive ESV report suitable to support NIST SP800-90b certification activities. An updated AN4230 document that includes the latest RNG results, methods, and recommendations for STM32 users. Keywords STM32 Embedded C RNG Entropy Source Validation Embedded Security, Cryptography Python and OpenSSL (Optional) 🔗 Link pour postuler: https://stmicroelectronics.eightfold.ai/careers?location=Tunis%2C%20Tunisia&pid=563637157282200&domain=stmicroelectronics.com&sort\_by=relevance&hl=en&triggerGoButton=false&triggerGoButton=true

Project_ID10 : GenAI Technical support - Data strategy on SNOW and Tools integration PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Purpose Valider le générative AI pour le support technique STM32 afin d'assurer des réponses précises, pertinentes et ponctuelles. Améliorer les processus d'assistance technique en automatisant les tests et en optimisant la capacité du modèle à traiter des requêtes, du dépannage et des guides utilisateurs liés aux MCU STM32. Travaux à réaliser / Tâches principales Automatiser l'exécution de cas de test AI et capturer les résultats en vue d'analyses reproductibles. Générer des rapports de test automatisés (ex: précision, complétude, types d'erreurs) et mettre en place des tableaux de bord de suivi. Mesurer la précision et la complétude des réponses générées par l'AI et suivre la fréquence et la typologie des erreurs. Intégrer et coordonner la stratégie de données sur SNOW (ServiceNow) et l'intégration avec outils externes pour centraliser logs, retours utilisateurs et métriques. Compétences techniques & Outils ciblés Travail avec STM32 et Embedded C pour comprendre le contexte technique des requêtes support liées aux MCU. Technologies Generative AI / LLM (OpenAI ou équivalents), Python pour scripting, tests automatiques et orchestration. Automatisation des tests AI, pipelines de données, et intégration d'outils (ex : ServiceNow / SNOW pour gestion des tickets et datas). Livrables attendus & critères d'évaluation Suite d'automatisation de tests AI exécutables et reproductibles (scripts, scénarios, jeux de tests) et génération automatique de rapports. Métriques claires sur précision, complétude, satisfaction utilisateur et typologie des erreurs, avec dashboards ou rapports périodiques. Proposition d'une stratégie de données sur SNOW et d'un plan d'intégration des outils pour améliorer le flux d'information entre support humain et AI. Contexte & mots-clés Mots-clés fournis : Embedded C and STM32 MCUs; Generative AI, LLM, OpenAI, Python; AI testing automation; Technical support optimization. Projet répertorié dans le PFE Book STTunis 2026, axé sur l'optimisation du support technique STM32 via des solutions GenAI. Lien pour postuler Pour postuler : https://stmicroelectronics.eightfold.ai/careers?location=Tunis%2C%20Tunisia&pid=563637157282200&domain=stmicroelectronics.com&sort\_by=relevance&hl=en&triggerGoButton=false&triggerGoButton=true

Project_ID09 Improving DCMIPP Pixel Pipeline Validation PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Purpose This internship project aims to optimize Embedded Software testing processes for the DCMIPP Pixel Pipeline. Objective is to intelligently select tests most likely to detect defects, reducing testing time and resource consumption while maintaining high software quality. Work To Be Done / Missions Analyze current validation tests and structure to identify inefficiencies and gaps. Define validation scenarios optimized for the pipeline use cases (pixel pipeline / ISP flow). Implement the defined scenarios and their relative environments to enable repeatable validation. Integrate the new tests into the existing validation environment and automation pipeline. Assess gains and coverage to quantify improvements in defect detection rate and test execution time. Technical environment & Keywords Target platforms and technologies: STM32 microcontroller family, DCMIPP, Pixel Pipeline, ISP. Work will involve embedded software validation techniques, test environment configuration, and coverage assessment. Deliverables & Evaluation A set of validated test scenarios and their configured environments integrated into the existing validation framework. Metrics and report detailing coverage improvements, detection gains, and resource/time reductions. Documentation and guidelines for maintaining and extending the scenarios in the pipeline.

Project_ID08 : LLM-Based Migration of Embedded Projects to STM32 PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Purpose / Objective Develop an AI-driven tool using Large Language Models (LLMs) to analyze and migrate embedded projects from non-STM32 MCUs to the most suitable STM32 device. Automate peripheral, feature and constraint mapping to speed up MCU selection and reduce manual porting effort. Main Tasks / Work to Be Done Structured Data Generation from source MCU documentation and drivers (extract datasheets, reference manuals, driver APIs and peripheral descriptions). Static and Dynamic Analysis of the embedded project to identify used peripherals, middleware, interrupts, clock configurations and system features. STM32 MCU selection based on feature matching and constraints (memory, peripherals, pinout, power, real-time requirements). Software emulation of missing hardware features where direct hardware mapping is not available. Integration of LLM-assisted automation tools to propose code refactorings, peripheral adaptation and migration patches. Validation and testing on STM32 hardware: deploy migrated firmware, run functional tests and compare behavior against original project. Keywords / Technologies / Deliverables Keywords: STM32, LLM, MCU Migration, Peripheral mapping, Code refactoring, Feature extraction. Expected technologies/tools: STM32 HAL/LL/CubeMX, static analysis tools, dynamic tracing/profiling tools, emulation frameworks, LLM frameworks or APIs (for assistant automation). Deliverables: a migration assistant/toolchain (prototype), migration reports for sample projects, test procedures and validation logs, documentation describing mapping rules and limitations. Validation & Evaluation Functional validation on target STM32 hardware with representative test cases from source projects. Quality metrics: correctness of peripheral mapping, compilation success rate, runtime behavior parity and documented manual interventions. Context / Constraints Work involves a combination of reverse-engineering (feature extraction), automated code transformation and hardware validation on STM32 platforms. Project requires careful handling of MCU-specific constraints (timing, DMA, interrupts, peripheral variations) and producing traceable migration decisions. Application Position referenced as: STTunis 2026 Project\_ID08 : LLM-Based Migration of Embedded Projects to STM32. To apply use the online link provided below.

Project_ID07 Secure Data exchange on external NOR flash PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Objectif du projet Implémenter une application permettant à des hôtes de confiance de communiquer de manière sécurisée avec un dispositif pour accéder aux données stockées sur une mémoire NOR Flash externe. Préparer les spécifications fonctionnelles et techniques nécessaires (SRS) avant le développement. Tâches principales Analyser les exigences et rédiger un document SRS détaillé couvrant le protocole de communication, les exigences de sécurité et les cas d'usage. Implémenter le protocole de communication entre l'hôte et le device basé sur des clés publiques (public-key based host-device protocol). Implémenter le chiffrement/déchiffrement côté device en utilisant la clé partagée HUK (Hardware Unique Key). Implémenter le chiffrement/déchiffrement côté host en utilisant des clés publiques. Développer une démonstration (demo) montrant l'échange sécurisé de données entre plusieurs hôtes de confiance et le device. Exigences techniques et compétences souhaitées Expérience en systèmes embarqués, idéalement sur STM32. Connaissances en cryptographie appliquée (gestion de clés publiques, chiffrement symétrique avec clés HUK/équivalentes). Compétences en gestion de mémoire externe (NOR Flash), fichiers et systèmes de fichiers embarqués. Compréhension des middlewares, protocoles de communication embarqués et bonnes pratiques de sécurité. Livrables attendus Document SRS complet décrivant architecture, protocoles, exigences de sécurité et cas de test. Code source implémentant : le protocole hôte-device, chiffrement/déchiffrement device (HUK), chiffrement/déchiffrement host (PK), et l'intégration avec la NOR Flash externe. Une démonstration fonctionnelle montrant l'accès sécurisé et l'échange de données entre plusieurs hôtes et le device. Rapport de tests et validations (scénarios d'attaque simples, test d'interopérabilité entre hôtes). Environnement et mots-clés Plateforme cible : STM32 (microcontrôleurs STMicroelectronics). Technologies / domaines : STM32, database, filesystem, middleware, NOR Flash, cryptographie à clés publiques, HUK. Contexte : STTunis 2026 Informations de candidature Lien pour postuler : https://stmicroelectronics.eightfold.ai/careers?location=Tunis%2C%20Tunisia&pid=563637157282200&domain=stmicroelectronics.com&sort\_by=relevance&hl=en&triggerGoButton=false&triggerGoButton=true

Project_ID06 AI-Driven Predictive Test Selection PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Purpose This internship project aims to optimize EmbSW testing processes by intelligently selecting tests most likely to detect defects. Goal is to reduce testing time and resource consumption while maintaining high software quality. Work To Be Done / Objectives Define: Analyze existing test suites and historical test execution data to identify patterns for predictive modeling. Design and develop: Create machine learning algorithms to predict the most relevant subset of tests to run based on recent code changes, historical failures, and risk factors. Enhance: Measure prediction accuracy, test coverage, and reduction in testing time to validate effectiveness. Responsibilities / Tasks Collect and preprocess historical test execution logs, code-change metadata (commits, diffs), and failure records. Engineer features representing test, code and risk attributes; evaluate different ML models and selection strategies. Integrate predictive selection logic into CI/CD pipelines (e.g., Jenkins) to automatically select and trigger the most relevant tests. Develop evaluation metrics and dashboards to measure prediction accuracy, test coverage, time savings and regression detection rate. Technical Environment & Keywords Relevant technologies: DevOps, AI, NLP, CI/CD, Jenkins, Git, Python, Robot Framework. Typical tools: Python ML libraries (scikit-learn, XGBoost, PyTorch/TF as needed), data processing (pandas), pipeline automation (Jenkins, Git hooks), test frameworks (Robot Framework). Expected Deliverables & Evaluation A reproducible ML prototype that selects a subset of tests given recent code changes and historical data. Quantitative evaluation showing prediction accuracy, impact on test coverage, and reduction in overall testing time. Integration proof-of-concept with CI/CD demonstrating automatic test selection and execution. Candidate Profile & Skills Strong background in machine learning and data analysis; experience with Python and ML libraries. Familiarity with software testing concepts, CI/CD tooling (Jenkins), and version control (Git). Good software engineering practices: data pipelines, reproducible experiments, and basic automation scripting. Location & Administrative Site: STTunis 2026 (project location indicated as Tunis) — internship listing and application are provided by STMicroelectronics. Link to apply and full job posting

Project_ID03 AI-powered Chatbot for Customer Support Using STMicroelectronics’ Public GitHub Data PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Purpose / Objectives Développer un chatbot IA pour le support client en utilisant les données publiques des dépôts GitHub de STMicroelectronics. Améliorer l'expérience utilisateur en automatisant les réponses aux questions techniques et en réduisant le temps de réponse. Travaux à réaliser / Tâches principales Collecter et prétraiter les données publiques provenant des dépôts GitHub de STMicroelectronics (extraction, nettoyage, normalisation). Définir et formater les données pour qu'elles soient compatibles avec les exigences d'entraînement des modèles d'IA (annotations, structure, tokenisation). Développer et entraîner un modèle de Generative AI (GenAI) capable de comprendre et de répondre aux requêtes clients. Implémenter une architecture Retrieval-Augmented Generation (RAG) pour combiner la récupération de documents pertinents et la génération afin d'améliorer la précision des réponses. Tester, évaluer et affiner le chatbot pour garantir fiabilité et pertinence dans des scénarios de support client. Approche technique / Méthodologie Pipeline de données : scraping/ingestion depuis GitHub public, nettoyage, création d'embeddings, indexation dans un store vectoriel pour retrieval. Modélisation : sélection et fine-tuning d'un LLM ou d'un modèle de génération adapté, engineering des prompts, définition des métriques d'évaluation (précision, pertinence, taux de réponse correcte). RAG : conception du flux retrieval + génération, tests de latence et pertinence, stratégies de ranking des documents récupérés. Livrables attendus Prototype fonctionnel du chatbot IA intégrant RAG, démontrable sur cas d'usage de support technique. Pipeline de prétraitement et jeu de données formaté prêt pour l'entraînement. Rapport technique détaillant les choix d'architecture, les résultats d'évaluation, et les recommandations d'amélioration. Documentation d'intégration et guide d'utilisation pour les équipes internes. Contexte & Mots-clés Contexte : STTunis 2026, utilisation de données publiques GitHub de STMicroelectronics. Mots-clés : AI chatbot, Generative AI, Data preprocessing, Model training, Retrieval-Augmented Generation (RAG), GitHub data, Customer support automation.

Project_ID02 STM32 Bootloader Tests Automation FW Enhancement PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Purpose This internship project aims to enhance the bootloader tests automation FW by integrating new features like continuous testing pipeline and enhancing existing ones. Improve automation coverage and reliability of the Bootloader (BL) Automated Testing Platform to streamline validation across product categories. Work to be done / Main tasks Define and validate projects templates (automated test environment) for each product category. Design and implementation of a Continuous Testing Pipeline using Jenkins for BL Automated Testing Platform. Enhance the bootloader test FW by integrating new features (OBK provisioning, SWD communication with embedded I3C host, auto fill of VTR document with tests results…). Technical scope / Technologies & keywords Embedded C, IAR, STM32, Bootloader development and testing. Python, C++, Qt, XML for tooling/automation, Excel automation, Jenkins for CI, Git for version control. Expected deliverables / Outcomes Validated automated test templates per product category and documented test environment setup. A working Continuous Testing Pipeline (Jenkins) integrated with the BL Automated Testing Platform. Enhanced bootloader test firmware implementing OBK provisioning, SWD embedded I3C host communication and automated VTR document population with test results. Skills & candidate profile Experience with embedded development (STM32, bootloader concepts) and test automation. Familiarity with CI/CD tools (Jenkins), scripting (Python), C/C++ development and test frameworks; ability to work with XML/Excel automation and version control (Git).

Project_ID04 STM32Cube ecosystem Benchmark PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Purpose This internship project aims to develop and validate comparable embedded application scenarios across different MCU software frameworks for benchmarking purposes. The goal is to enable static and functional analysis to highlight strengths and improvement opportunities, supporting strategic decisions for enhancing the ST embedded software ecosystem. Work to be done / Tasks Develop similar scenarios on ST and peer MCU platforms using their embedded software frameworks (implement equivalent application use-cases across platforms). Evaluate code quality metrics such as coverage, simplicity, integration, consistency and portability for each implemented scenario. Conduct functional testing to assess performance, memory footprint and robustness of the scenarios across different platforms. Perform static analysis of codebases and record measurable indicators to compare frameworks. Deliverables & Reporting Compile a comprehensive benchmark report summarizing results, quantitative measurements, insights and concrete recommendations for ST framework improvements. Provide comparative tables/graphs showing metric results (coverage, performance, memory usage, portability, etc.) and documented test procedures. Include suggested action items and prioritised enhancements for the STM32Cube ecosystem based on the analyses. Skills & Keywords Required/Relevant skills: Embedded C development, static analysis tools, MCU performance profiling, familiarity with MCU software frameworks (STM32Cube and peer frameworks). Keywords: Embedded C, Static Analysis, MCU performance, Software Frameworks, Code Quality, Benchmarking. Context & Impact The benchmark will support strategic decisions for ST embedded software ecosystem enhancement by identifying strengths and improvement opportunities across frameworks. Work contributes directly to improving quality, portability and robustness of embedded software offerings for STM32 and comparable MCUs.

Project_ID01 FPGA Non-Regression Workflow Automation PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Purpose and Context This internship aims to develop an automated framework to streamline the non-regression testing process for FPGA validation of STM32 MCUs. The solution must enable automatic updates, builds, and functional test executions triggered by relevant project changes, ensuring continuous validation of software and FPGA netlist releases. Work to be Done / Main Tasks Analyze and design an automated non-regression testing workflow on the FPGA platform, covering environment setup, test execution and reporting. Implement CI/CD jobs to trigger builds and tests based on project updates and automate the entire flow from source changes to test results. Automate setup of the FPGA platform environment, execution of functional tests and generation of HTML summary reports of results. Technical Requirements & Tools Expected technologies and languages: C, Python, Bash, JSON; embedded toolchain experience (IAR) is a plus. DevOps/CI knowledge: Git, CI/CD pipelines, Jenkins (implement and configure jobs to run builds/tests), scripting for automation. Expected Deliverables & Outcomes A reproducible automated non-regression framework that triggers builds/tests upon changes and reports results with clear HTML summaries. CI/CD job definitions (e.g., Jenkins pipelines), automation scripts for environment setup and test orchestration, and documentation describing usage and integration. Keywords / Relevant Areas C, Python, Git, Bash, IAR, JSON, CI/CD, Jenkins FPGA validation for STM32 MCUs, non-regression testing, automation

Project_ID05 AI-Powered Chatbot and Virtual Assistant for DevOps PFE

STMicroelectronics •

Hybride4-6 moisExpire dans 14 jours

Purpose Streamline and automate routine DevOps operations by leveraging conversational AI. Reduce manual effort, accelerate response times and improve overall operational efficiency. Work to be done / Tasks Analyze current DevOps workflows and identify repetitive requests suitable for automation via chatbots or virtual assistants (builds, incident reporting, FAQ handling). Design and develop an intelligent chatbot or virtual assistant integrated with DevOps tools (for example Jenkins, GitHub Actions) to assist in build incident management, perform automatic diagnosis and trigger automated remediation actions. Implement and enhance NLP capabilities to improve user interaction, handle natural language queries and extend functionalities based on user feedback and evolving DevOps practices. Technical scope & Tools Integrate with CI/CD and DevOps tooling such as Jenkins, Git (GitHub Actions), and CI pipelines; implement APIs or connectors as needed. Use AI/NLP techniques and tools (Python-based stacks expected) for intent recognition, entity extraction, dialogue management and automated decisioning. Implement logging, monitoring and safety checks for automated actions to prevent unintended changes in production environments. Expected deliverables A working prototype chatbot/virtual assistant integrated with at least one CI/CD tool demonstrating incident diagnosis, FAQ answering and automated remediation actions. Documentation covering architecture, integration points, NLP models, testing results and recommendations for production rollout. Keywords / Skills DevOps, AI, NLP, CI/CD, Jenkins, Git, Python Experience or strong interest in conversational AI, automation workflows and practical integration with DevOps pipelines. Application STunis 2026 — Project\_ID05 Link to apply is provided below.