AI / Machine Learning Rsearch intern (x/f/m) Nouveau

AI / Machine Learning Rsearch intern (x/f/m) We are looking for an M2 Intern to join the PreMeDICaL team at Inria Montpellier. As an M2 Intern, your mission will be to work on developing causal frameworks to evaluate artificial intelligence in medical contexts. This 6-month internship offers the opportunity to continue with a 3-year PhD position. The PreMeDICaL team is an Inria‑Inserm research unit based in Montpellier, developing statistical and machine learning methods for precision medicine, in close collaboration with machine learning researchers and clinicians. This research project is conducted in collaboration with Doctolib, Europe's leading healthcare technology platform. In healthcare, AI systems never operate in isolation—clinical decisions remain under human authority. This creates complex dynamics between AI systems and healthcare professionals. You will investigate the causal mechanisms underlying these human‑AI interactions, where AI predictions influence clinician decisions and, consequently, patient outcomes. Your responsibilities include but are not limited to: Developing causal frameworks to evaluate AI beyond accuracy, using data from randomized trials and observational cohorts Analyzing how AI predictions influence clinical decisions and patient outcomes in real‑world medical contexts Collaborating on concrete use cases Contributing to theoretical research on performative learning and policy learning in the context of AI deployment Publishing your results in top‑tier scientific conferences and journals Who you are Before you read on: if you don't have the exact profile described below, but you feel this job description matches your skill set, we still encourage you to apply. You are currently enrolled in an M2 program in a relevant field (machine learning, statistics, computer science, applied mathematics) You have a solid foundation in machine learning and statistical inference You are interested in medical applications and the real‑world impact of AI in healthcare You are fluent in English (written and spoken) for scientific publications You have a rigorous approach to research and an appetite for interdisciplinary collaboration Now it would be fantastic if you: Have knowledge of causal inference Have experience in machine learning or medical data analysis Are familiar with scientific programming (Python, R) What we offer A flexible workplace policy offering both hybrid and office‑based mode Lunch voucher with Swile card Work Council subsidy to refund part of sport club membership or creative class Reimbursement of public transportation The interview process Interview with a recruiter: An exchange to discover your background, motivations, and assess the fit of your skills with our needs Technical interview with supervisors: In‑depth discussion about your research skills and scientific interests Project presentation or research discussion: Exchange about your previous work or ideas for the project 6‑month internship (M2) Full‑Time Paris For more detailed information about this research opportunity, please consult the full internship proposal: HERE. At Doctolib, we are committed to improving access to healthcare for everyone. This translates into our recruitment process. We evaluate candidates based solely on qualifications and motivation, without any form of discrimination. The more diverse ideas are heard, the more our product will truly improve healthcare for all. You are welcome to apply to Doctolib, regardless of your gender, religion, age, sexual orientation, ethnicity, disability. To ensure equal opportunities, we invite you to exclude personal information (e.g., pictures, age) from your applications. If you require any accommodation, please let us know for support during the hiring process. Join us in building the healthcare we all dream of All information provided is processed by Doctolib for application management. For data processing details, click here. Please contact hr.dataprivacy(at)doctolib.com for inquiries or to exercise your rights. #J-18808-Ljbffr