Our stages are exclusively for students! If you’re a bachelor’s or master’s student looking for a stage to do your thesis or to earn university credits, this opportunity is for you.
To apply, you must be currently enrolled in a recognized university program. Unfortunately, if you’ve already graduated or are not a student, you won’t be eligible for these positions.
Intelligent DevEnvironment for Modern Teams
[26001]
(First availability: From 1st of September, 2026)
Onboarding new software development teams usually faces some challenges. Setting up a new project environment often involves configuring dependencies, installing extensions, aligning development standards, and understanding project structure. This process may take days and even introduce inconsistencies that affect productivity and code quality. For these reasons modern software development teams require fast, reliable and intelligent onboarding processes.
Cardiac Diagnostic Assistance Agent
[26004]
(First availability: From 1st of September, 2026)
Cardiologists routinely analyze heterogeneous data sources such as ECG tracings, Doppler echocardiography reports, and electronic health records (EHRs). Integrating these inputs into a coherent clinical interpretation requires significant expertise and careful cross-referencing across multiple systems and formats.
This project aims to develop a fully offline prototype software system based on MedGemma, designed to support the integrated analysis of multimodal cardiac data. The system will transform ECG signals, echocardiographic findings, and relevant EHR data into concise and structured clinical assessments. It will generate explainable diagnostic reports that clearly document the evidence supporting each output, allowing clinicians to easily trace conclusions back to specific signal features, extracted parameters, or contextual patient information.
The prototype will follow a privacy-by-design architecture. All processing will occur in memory, without persistence of sensitive patient data. Optional network isolation will ensure that no data leaves the local environment, and logging will be limited strictly to anonymized performance metrics.
Industrial Machine Control via FunctionGemma and Voice Interfaces
[26002]
(First availability: From 22nd of July, 2026)
In industrial environments, machines are usually controlled through physical panels, touchscreens, or supervisory systems. These interfaces are reliable, but they are not always the most efficient or ergonomic, especially in fast-paced or hands-busy scenarios. Voice interaction could make operations faster and more intuitive, but in an industrial context, safety and predictability are far more important than convenience.
This project aims to prototype a voice-driven Human-Machine Interface (HMI) that allows operators to control equipment using speech while maintaining strict safety and reliability standards. The system will use Google’s FunctionGemma to map spoken commands into predefined machine functions in a controlled and deterministic way. Speech recognition and synthesis will run locally to ensure low latency, resilience, and data privacy. The focus is not just on “voice control,” but on building a structured and auditable system where every action is validated before it reaches the machine.
Automated Regulatory Documentation System for Medical Devices
[26003]
(First availability: From 1st of September, 2026)
Medical device manufacturers operating under the Medical Device Regulation (MDR) and In Vitro Diagnostic Regulation (IVDR) frameworks, are required to produce extensive technical documentation demonstrating safety, performance, and regulatory compliance.
In many organizations, this documentation is still partially managed through manual editing of Word or PDF files, which increases the risk of inconsistencies, broken traceability, and non-compliant changes.
This project aims to develop an Automated Regulatory Documentation System that enforces a Single Source of Truth (SSOT) for all regulatory artifacts. The system will generate compliant documents programmatically, validate cross-references automatically, and maintain a complete traceability graph linking requirements, hazards, specifications, and verification evidence.
A central innovation of the system is the technical enforcement of document integrity: final documents will include regulatory watermarks containing Git commit hashes, making any manual or untracked modification immediately detectable. The objective is to eliminate undocumented changes and guarantee full traceability for certification and audit processes.
Industrial Machine Control via PicoClaw/GoClaw and Multimodal Feedback
[26008]
(First availability: From September, 2026)
In industrial environments, machines are commonly controlled through physical panels, touchscreens, PLC interfaces, or supervisory systems. These interfaces are reliable and widely adopted, but they may not always provide the most efficient interaction model in fast-paced, hands-busy, or high-attention operational scenarios.
Voice-based interaction can give operators a more natural and direct way to talk to machines. However, in an industrial context, voice control cannot be designed as a generic conversational assistant. Machine commands must be predictable, validated, traceable, and safe. Any ambiguity or incorrect interpretation could affect productivity, equipment reliability, or operator safety.
This project aims to design and prototype a multimodal Human-Machine Interface, HMI, that allows operators to control industrial equipment through voice commands while receiving immediate visual and/or acoustic feedback. The system will use PicoClaw/GoClaw to map recognized user intents to a constrained set of predefined machine-executable functions. This deterministic approach ensures that commands are not interpreted freely but are resolved only into allowed and validated operations.
AI-Driven Dynamic Dashboard Generation from Natural Language and Data Schemas
[26009]
(First availability: From September, 2026)
In modern industrial and cloud-native environments, dashboards are essential tools for monitoring operations, analyzing system behavior, and supporting decision-making. They allow users to visualize data coming from machines, databases, APIs, and event streams in a clear and actionable way. However, designing and implementing dashboards is still often a manual and time-consuming activity that requires both domain knowledge and technical expertise in backend development, data querying, and frontend UI implementation.
This project aims to prototype a system that uses Large Language Models (LLMs) to automatically generate dashboards, or parts of dashboards, starting from human-friendly textual requests. The system will interpret natural language inputs such as “show machine efficiency trends over the last 24 hours” and combine them with structured data descriptions, including SQL schemas, JSON schemas, API definitions, or time-series metadata.
Reverse Engineering of an Existing Gantry-Robot
[24013]
(First availability: To Be Agreed)
The project focuses on three key objectives. First, reducing costs by identifying affordable yet reliable components. Second, enhancing the robot’s performance to meet modern industrial needs. Third, providing clear documentation to simplify replication and deployment.
This project aims to redesign and improve a gantry robot to reduce costs and enhance its performance. The process involves studying the current robot, creating detailed 3D models, and exploring alternative components to optimize its design. The ultimate goal is to deliver a solution that is affordable, scalable, and ready for practical use across various applications, all while ensuring thorough documentation for future use.
Door Vertical Movement Mechanis
[25020]
(First availability: To Be Agreed)
This project focuses on the design and development of a new vertical movement mechanism for liquid handler instrument doors, a solution that is particularly appreciated in laboratory environments where space is limited. While most vertical doors rely on gas springs and do not operate with true vertical motion, laboratories often require compact, ergonomic, and reliable solutions.
M31 has so far used a mechanism derived from the furniture industry, which, although functional, is not well suited for industrial use. The goal of this stage is to provide a complete vertical aperture solution that integrates essential features such as manual or electrical control, door balance to maintain positioning, smooth movement, shock absorption, a guiding and locking system, and the possibility of incorporating sensor triggers. Supported by M31’s hardware, firmware, and software expertise, the project will ultimately deliver a new generation of vertical door mechanisms specifically adapted to industrial and laboratory needs.
If you are interested in a freer approach or if you want to propose an idea yourself, take a look at our Lab or simply contact us!
These are the Academy initiatives available! If you want to consult the initiatives already carried out in the past, visit our Project Repository!