Embedded AI Intern  

The focus of this internship will be the predictive detection of air in the system: from identifying micro-bubbles that reduce efficiency and cause wear (induced cavitation), to the instant detection of dry running.

During this experience, the candidate will have the opportunity to bring Artificial Intelligence into the strict physical and computational constraints of industrial electronics.

How you will support the team

The candidate, working within the Software/Firmware development team, will manage the entire lifecycle of a Machine Learning model for embedded systems:

• Laboratory Data Collection: Design and execution of bench tests on real pumps to generate physical phenomena (controlled air injection, dry running). Acquisition of electrical and mechanical reference signals;
• Data Analysis & Pre-processing: Time and frequency domain analysis (FFT, Order Tracking) of phase currents and torque current ($I_q$) to identify ?signatures? (features) related to the presence of air;
• ML Model Training: Development of classification or anomaly detection models in a Python environment (TensorFlow Lite, Scikit-learn);
• Embedded Deployment: Use of the Renesas e-AI environment to convert, optimize, and integrate the trained neural network directly into the C source code of the RX series microcontroller;
• Validation: Field testing of the generated firmware to validate execution time, memory usage (RAM/Flash), and real-time reliability of the algorithm.

Who you are

• Master's student or recent graduate in Electronic Engineering, Computer Engineering, Automation Engineering, Mechatronics, Physics, or related fields;
• Good knowledge of the C programming language and microcontroller architectures;
• Basic knowledge of Python and main data analysis libraries (Pandas, NumPy);
• Understanding of Machine Learning / Neural Network fundamentals.
• Nice to Have (Preferred Skills):
• Basics of Digital Signal Processing (digital filters, Fourier transforms);
• Knowledge of electric motor control (inverters, FOC, PID);
• Previous experience, even academic, with real-time systems or RTOS;
• Analytical and experimental (hands-on) approach, problem-solving mindset, and ability to work closely with hardware in a lab environment

Why you should join us:

You will become an integral part of our Global Team where people are friendly and work together in a stimulating and fast-changing environment.

You will get in contact with our colleagues worldwide and closely work with other departments, getting the overview about how a multinational organization works.

Training will be core since the beginning: you will learn to use our main enterprise tools, in particular ...(es. LN Infor (ERP system).

Finally, we are offering monthly reimbursement from €600/monthly (gross), free canteen and IT toolset.