Doctoral researcher in clinical data science and machine learning

We are looking for a highly motivated PhD candidate interested in AI-based methods, including machine learning and language technologies, for the integration and analysis of clinical, advanced data harmonisation, and next generation research infrastructures. You will contribute to research projects enabling secure, interoperable, and scalable AI-driven use of clinical data for complex diseases such as Alzheimer’s and Parkinson’s.

You will work within a multidisciplinary environment alongside data scientists, software engineers, biomedical researchers, and clinicians. Your research will focus on developing AI- and LLM-enabled methods and tools to structure, harmonise, and analyse clinical data in a FAIR, privacy-preserving, and clinically meaningful manner, with particular attention to unstructured and multimodal data.

Your responsibilities include:

• Developing and applying machine learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data
• Designing and implementing NLP pipelines for clinical text processing, semantic annotation, and representation learning
• Developing embedding-based representations of clinical variables and documents to support semantic harmonisation and retrieval
• Designing and implementing Retrieval-Augmented Generation pipelines that combine structured clinical data with unstructured text and external knowledge sources
• Designing and implementing data structuring and harmonisation pipelines, including semantic mapping to clinical data models and ontologies e.g. OMOP CDM, SNOMED CT, FHIR
• Contributing to interoperable and FAIR-compliant data infrastructures that enable secure data sharing, reuse, and AI-driven analytics
• Exploring and implementing federated learning and privacy-preserving AI approaches for distributed clinical datasets
• Collaborating closely with data providers, clinicians, and technical teams to ensure high-quality data integration, validation, and analysis workflows
• Supporting documentation, reproducibility, and dissemination of research outputs e.g. scientific publications, presentations, consortium deliverables
• Participating in international research collaborations and contributing to large-scale EU funded research initiatives

For further information, please contact Dr. Sandrine Medves (email address: sandrine.medves@uni.lu ) . Use the job reference number mentioned above in the subject line of your email.