Investigador/a Predoctoral IA para Optimización de la Cadena de Suministro Farmacéutica

About the role

Universidad Loyola is seeking a predoctoral researcher to lead an industrial PhD project focused on applying Graph Neural Networks and spatio‑temporal deep learning models to optimise the pharmaceutical supply chain in collaboration with Pfizer.

Key responsibilities

• Extract, clean and transform heterogeneous, dynamic supply‑chain data (SKUs, plant capacities, lead times, external events) into high‑quality temporal heterogeneous graphs.
• Design, implement and evaluate deep‑learning architectures such as ST‑GNNs that combine advanced graph blocks for supply‑chain optimisation.
• Collaborate with academic supervisors and Pfizer’s industrial team to align research objectives and deliver scientific publications.
• Document data pipelines, model configurations and experimental results for reproducibility.

Required profile

• Official Bachelor’s and Master’s degree in Artificial Intelligence, Data Science, Engineering, Mathematics or a related STEM field, enabling enrolment in a PhD programme.
• English proficiency at C1 level for literature review and international scientific writing.
• Strong foundation in deep learning and predictive analytics.
• Proactive attitude for multidisciplinary collaboration between academia and industry.

Required skills

• Advanced Python programming.
• Experience with deep‑learning frameworks, especially PyTorch.
• Knowledge of graph‑neural‑network libraries and concepts.
• Data preprocessing and transformation of complex, dynamic datasets.

What we offer

• Fixed‑term, full‑time contract linked to a four‑year PhD thesis (18,876 € gross per year).
• Opportunity to work on a cutting‑edge AI project with a global pharmaceutical leader.
• Access to university research facilities and academic supervision.
• Potential for co‑authored scientific publications and international conference presentations.