Mots-Clés

Deep learning, Clinical Data, AI for health

Description

Electronic health records provide a platform to enhance personalized medicine on an epidemiological scale. When combined with other modalities such as genomics, environmental, genetic, or lifestyle information, they offer an exciting opportunity to improve disease risk prediction and personalize treatment. However, optimal multimodal integration remains an open question, and advanced methods are needed to effectively leverage this data. The objective is to develop innovative deep learning techniques to comprehensively and efficiently handle such data, improving precision medicine tasks, including disease etiology characterization, risk prediction, and actionable target discovery.

Research Group

The doctoral student will be supervised by Dr. Eloise Berson (Assistant Professor) (eloise.berson@minesparis.psl.eu).

The project will take place in the Centre for Computational Biology (CBIO — http://cbio.ensmp.fr),

a joint laboratory between Mines-PSL, one of the most prominent French engineering

schools, and Institut Curie, a major hospital and research facility dedicated to cancer. CBIO

benefits from an exceptional scientific environment with immediate access to experts and

collaborators in biology and medicine, enabling a stimulating interdisciplinary exchange. The

laboratory is located in the center of Paris, both in Mines PSL and in the nearby Institut

Curie.

Prerequisites

• A Master's degree in Computer Science, Machine learning, Applied Mathematics, Engineering, or Medical science;
• Strong knowledge of deep learning and/or statistics;
• Proficiency in at least one programming language;
• Prior experience in biology or the medical field is not required.

Required Application Materials 

Please send your CV, a cover letter (describing relevant experiences, interests, and goals), and two contact references by email at eloise.berson@minesparis.psl.eu.

We strongly encourage individuals from historically underrepresented groups to apply. We believe that equity and inclusion are fundamental not only for the science that we do but also for fostering a workplace where everyone feels valued and supported. Our commitment is to create an environment where equal opportunities thrive, and where your unique experiences and background will be highly appreciated. We welcome your application and look forward to the contributions you can bring to science.