Missions

The European Genomics Institute for Diabetes (Egid) seeks a talented post-doc to develop artificial intelligence tools to better understand, predict, prevent, and target diabetes and related metabolic diseases, particularly Metabolic dysfunction-associated steatotic liver disease (MASLD). The successful applicant will be part of the UMR1283/8199 led by Professor Philippe Froguel, located at the CHU of Lille, internationally recognised for their research on the topic. They will join the team “Metabolic functional (epi)genomics and their abnormalities in type 2 diabetes and related disorders”, an interdisciplinary group of experts in genomics and computational biology led by Amélie Bonnefond.

Obesity and type 2 diabetes are accompanied by chronic inflammation affecting multiple organs, such as the liver. The prevalence of type 2 diabetes and hepatic steatosis is rising. It is therefore critical to predict their risk, and understand their causes to better diagnose and treat them with targeted therapies. The project will be carried out along two axes, one focusing on type 2 diabetes, the other on MASLD. In both cases, the candidate will develop models trained on multi-omics and clinical data aimed at predicting disease risk, grouping patients for better treatment, and analyzing relationships between molecular phenotypes. The models will use architectures featuring attention structures, which - in addition to improved performance - will enable the model to be interrogated to understand the reasons for its decisions.

Activities

The successful applicant will be involved in all aspects of the design, implementation, and deployment of AI solutions, including :
• Preparation and analysis of heterogeneous datasets, including clinical, genetics, (epi)genomics, transcriptomics, and metabolomics data ;
• Design, development, and deployment of multi-modal neural networks, based on several architecture and trained on heterogeneous data ;
• Assessment and validation of the models' predictions in collaboration with biological and clinical experts ;
• Use of interpretable AI methods to extract meaningful features from the models and derive new biological insights, such as molecular networks underpinning the metabolic disorders.
The postdoctoral fellow be responsible for the preparation, interpretation, and dissemination of results, including writing research articles and presenting in conferences.

Skills

We are looking for an expert in artificial intelligence already trained in Deep Learning approaches, and able to hit the ground running. A good knowledge of molecular and cellular biology or physiology is appreciated but not mandatory.
• AI-related PhD in computer science, mathematics, physics, or bioinformatics ;
• Excellent knowledge of Deep Learning, the underlying concepts, the different architectures, including Auto-encoders, Transformers and Large Language Models ;
• Proficiency in Python programming, with good knowledge of the Tensor/Keras or the Pytorch universe ; knowledge of R programming appreciated ;
• Good knowledge of a variety of machine learning approaches ;
• Good experience of Unix/Linux and working with distributed computing infrastructures ;
• Mastery of the English language (written and spoken) ;
• Curiosity, rigour, and desire to work in a highly collaborative environment.

Work Context

The team “Metabolic functional (epi)genomics and their abnormalities in type 2 diabetes and related disorders” is an interdisciplinary and very collaborative group of experts in genomics and computational biology recognised worldwide, with a growing AI activity. The succesfull applicant will have access to unique cohorts of patients providing clinical, genomic, and functional genomic data. The research is supported by technological platforms and supporting staff, such as NGS, metabolomics, bioinformatics, and biostatistics.
On top of an excellent intellectual environment the institute provides the infrastructure required to carry out the project, with direct access to state-of-the-art platforms including large computing clusters with thousands of CPUs and GPUs, together with petabytes of storage.
UMR1283/8199 http://www.good.cnrs.fr/
EGID https://egid.fr/