In today’s technological landscape, AI has become one of the most widely utilized and sought-after assets. While there is no shortage of innovative ideas in AI research and applications, two major challenges persist: the demand for computational power and the need for sufficient data. While the first challenge can be addressed by investing in more GPUs, the second—obtaining more data—is not always as straightforward. One solution is leveraging AI models to generate synthetic data, which can then be used to train other models. However, this introduces a new challenge: traditional methods for validating data quality assume that the data is real. As a result, before utilizing synthetic data, it’s essential to determine how closely it resembles real data or whether it can convincingly pass as real. With the advent of new generative models capable of producing high-quality synthetic data at scale, ensuring the reliability of this data before using it to train models has become critical. Poor-quality data can lead to misclassification, bias, and the degradation of previously well-functioning models. Key questions arise in this context: How close is the synthetic data to real data? And what does “close enough” mean? Is each generated sample of consistently high quality? If the synthetic data is flawed, is the issue with the generative model or the real data used to seed it? These are complex questions, and they represent the current frontier of research in generative data quality. The intern will play a crucial role in contributing to the development of a framework for assessing the quality of generative deep learning models. Responsibilities include: Conducting a thorough literature review on quality metrics in generative AI; Familiarize and apply the quality assessment framework developed by Multitel; Enhancing the framework by implementing cutting-edge quality evaluation methods; Benchmarking the performance of different methods across various datasets; Compiling findings into a comprehensive report; Presenting results to AI experts within Multitel. Expected qualifications: A strong aptitude for identifying innovative techniques through in-depth literature review, proficiency in Python (particularly with PyTorch), the ability to translate mathematical concepts into functional code, good english level. A 4 months internship (from February until end of April 2025) CV and cover letter requested. You will work in a dynamic and welcoming environment. You’ll be part of a friendly and energetic team. We offer a stimulating and supportive work environment. Recognized as a centre of excellence at the international level, Multitel develops and integrates emerging technologies in the industrial sector. These technologies are focused on five main areas of activity: Networks and Cybersecurity, Applied Photonics, Artificial Intelligence, Embedded Systems, and Railway Certification Within the Artificial Intelligence department, the AI team holds a crucial role in supporting companies with their technological innovation projects, guiding them from the feasibility phase to the minimum viable product in various application fields such as aerospace, automotive, Smart Cities, Industry 4.0, medical sciences, defense, and more. We love fresh ideas, shared coffees, and motivated people. If you’re eager to learn fast and make a real impact, join us!