Research Assistant in Robust Machine LearningCUNEF Universidad | Madrid, Spain | 12 months CUNEF Universidad is hiring a Research Assistant to work with me on LUAR (learning under adversarial risks), a project funded by a 2026 Leonardo Grant from Fundación BBVA. The ProjectLUAR addresses two questions on the robustness and security of machine learning (ML) systems: How much can an attacker alter the training data or inputs of an ML system before it recommends a different decision? How can we robustify these decisions by anticipating the attacker? The project explores these questions across a range of high-stakes applications. More information about the project: https://www.redleonardo.es/beneficiario/roi-naveiro-flores/ Main Responsibilities
• Implement and reproduce ML baselines in Python.
• Design and run controlled experiments on how adversarial perturbations affect model predictions and downstream decisions.
• Evaluate defenses and communicate results through figures, short research notes, and reproducible code. Candidate ProfileCandidates should meet the following

requirements:
• A master’s degree (completed or near completion) in statistics, computer science, mathematics, physics, engineering, or a related quantitative field.
• Strong Python skills and experience implementing and evaluating ML models.
• A high level of English, sufficient to take part in technical discussions and to document their work clearly. The following are desirable:
• Familiarity with Bayesian methods,



adversarial ML, or decision theory.
• Experience with PyTorch or JAX, and with probabilistic programming languages (e.g., Pyro or Stan). Terms12 months in Madrid. Preferred start in December 2026 or early January 2027.

How to Apply
Apply by email to [email protected] by November 15th with the subject “LUAR RA Application: [Your Name]”. Attach a single PDF file, in English, containing the following documents in this order:
• A CV of up to two pages, including a link to your GitHub profile.
• A motivation letter of up to one page, stating your earliest start date.
• An academic transcript from your undergraduate degree.
• One reference letter from someone who can speak to your academic and/or research abilities (optional). Shortlisted candidates will be invited to an online interview. Prior to the interview, candidates will be asked to submit one work sample (e.g., a report or code from coursework or a thesis), with a brief paragraph detailing their contribution. Links to code repositories are acceptable as work samples. CUNEF Universidad is committed to equal opportunity. Personal data will be processed under CUNEF Universidad’s privacy policy: https://www.cunef.edu/en/privacy-policy/ Questions welcome at [email protected]. Proyecto realizado con la Beca Leonardo de Investigación Científica y Creación Cultural 2026 de la Fundación BBVA. #Hiring #ResearchAssistant #MachineLearning

📌 Asistente de investigación (Madrid)
🏢 CUNEF Universidad
📍 Madrid