Chair of Excellence in Artificial Intelligence for Drug Discovery Project: MOSAIC-Health ‒ Multimodal and Explainable Artificial Intelligence for Drug Discovery and Digital Health Education

Offer Description

Scientific context

Université de Caen Normandie is seeking an internationally recognized scientist to lead the MOSAIC-Health Chair of Excellence at CERMN.

CERMN is a research unit dedicated to drug discovery and the characterization of biologically active compounds. Its Chemoinformatics and Biostructural Group develops computational approaches for the analysis of chemical and pharmacological data, ligand–target relationships, structure–activity relationships, pharmacophore modeling, and molecular design.

MOSAIC-Health will strengthen this activity through the development and application of modern artificial intelligence methods for drug discovery. The project will build on existing collaborations with GREYC (UMR CNRS 6072), the computer science laboratory of Université de Caen Normandie, particularly in machine learning and graph-based approaches. Depending on the scientific questions addressed, the Chair may also interact with local expertise and technological platforms in bioinformatics, metabolomics, proteomics, transcriptomics, and multi-omics.

Scientific objectives of the Chair

The Chairholder will develop an internationally visible research program in AI-assisted drug discovery at the interface of chemoinformatics, molecular modeling, machine learning, and experimental pharmaceutical research.

Chemical structure will remain the primary point of entry. Depending on the application, molecular representations may be combined with protein, interaction, pharmacological, phenotypic, or omics information when these additional data provide a demonstrable scientific benefit.

Particular attention will be given to the reliability of AI methods. Predictive performance will therefore be considered together with data quality, uncertainty, applicability domains, interpretability, reproducibility, and appropriate comparison with baseline methods.

Predictive modeling will constitute a major component of the research program. Advanced molecular representations, graph-based learning, multimodal approaches, and generative methods may also be investigated when they address clearly defined drug-discovery questions and can be evaluated under relevant chemical and biological constraints.

A central feature of MOSAIC-Health will be the connection between computational modeling and experimentation. Computational results will be used to prioritize compounds, explore chemical space, identify molecular series of interest, and formulate experimentally testable hypotheses. Selected compounds may subsequently be purchased or synthesized and subjected to biological evaluation. Promising results may lead to medicinal chemistry studies aimed at characterizing structure–activity relationships and optimizing selected molecular series.

Main responsibilities

The Chairholder will provide scientific leadership for the AI-assisted drug-discovery activities developed within MOSAIC-Health.

They will be expected to:

• develop and evaluate advanced AI and machine-learning approaches for molecular and pharmaceutical data;
• lead the methodological and drug-discovery components of the MOSAIC-Health program;
• identify and develop research case studies jointly with CERMN chemists;
• establish rigorous strategies for data preparation, model evaluation, uncertainty assessment, interpretability, and reproducibility;
• supervise and coordinate the scientific activities of a PhD candidate and a research engineer recruited within the project;
• strengthen scientific interactions between CERMN and GREYC and develop collaborations with other academic, clinical, and industrial partners;
• contribute to peer-reviewed publications, international scientific meetings, and the dissemination of research outputs;
• develop national and international collaborative research proposals and contribute to securing follow-on funding;
• contribute to the long-term development of AI-assisted drug discovery at CERMN beyond the initial Chair funding period.

Education and training

The Chairholder will also contribute to advanced education in AI-assisted drug design and pharmaceutical research at Université de Caen Normandie.

Teaching activities may address topics such as chemoinformatics, molecular representations, machine learning for molecular data, predictive modeling, compound prioritization, model reliability, and the critical interpretation of computational results.

These activities will complement the broader digital-health education developed through the SATIN program and will connect research conducted within MOSAIC-Health with relevant programs in pharmaceutical sciences and computer science, including the Master’s programs in Drug Design and in Artificial Intelligence, Data Science, and Health.

The Chairholder may also supervise Master’s projects and research internships and contribute to doctoral training.

Research environment and resources

The Chairholder will join CERMN at Université de Caen Normandie and will have access to the laboratory’s expertise in chemoinformatics, medicinal chemistry, molecular design, pharmacology, chemical and pharmacological data resources, compound library, and experimental capabilities.

The MOSAIC-Health project will provide a dedicated research environment including:

• one PhD candidate recruited for 36 months;
• one research engineer recruited for 36 months;
• a dedicated GPU-equipped computing workstation and data-storage resources;
• access to molecular-modeling and chemoinformatics software;
• resources for the acquisition and experimental evaluation of prioritized compounds;
• resources for focused medicinal chemistry and hit-to-lead activities;
• access, where scientifically relevant, to complementary expertise in machine learning, bioinformatics, and omics within Université de Caen Normandie.

The Chairholder will benefit from established interactions with GREYC and from the wider scientific environment of the Caen health sciences campus.

Expected contribution to the host institution

Beyond the scientific results obtained during the 42-month project, the Chairholder is expected to help establish a durable activity in AI-assisted drug discovery at CERMN.

The position is therefore intended for a scientist willing to become strongly involved in the scientific life of the host laboratory, develop collaborations within and beyond Université de Caen Normandie, contribute to advanced training, and participate in building competitive national and international research proposals.

Where to apply

E-mail: alban.lepailleur@unicaen.fr

Requirements

Research Field: Pharmacological sciences

Education Level: PhD or equivalent

Skills/Qualifications

PhD or equivalent in Artificial Intelligence for Health, Chemoinformatics, AI-Assisted Drug Discovery.

Applicants should be senior scientists with an excellent international research record, or researchers with demonstrated potential to achieve a leading international position in the field.

Candidates must hold a PhD or equivalent doctoral degree in a field relevant to the Chair, such as chemoinformatics, computational chemistry, molecular modeling, artificial intelligence, machine learning, computer science, computational biology, or a closely related discipline.

A strong candidate is expected to demonstrate:

• an internationally visible publication record in AI-assisted drug discovery, chemoinformatics, molecular modeling, or a closely related field;
• strong expertise in machine learning applied to molecular, chemical, pharmacological, or biological data;
• experience in the development and critical evaluation of predictive computational models;
• a strong understanding of molecular and pharmaceutical research questions;
• experience working in interdisciplinary environments involving computational and experimental scientists;
• the ability to lead research projects and supervise early-career researchers;
• experience in developing national or international scientific collaborations;
• the ability to communicate and teach advanced scientific concepts to students from different disciplinary backgrounds;
• the ability to work effectively in an international research environment.

Additional expertise in graph-based or multimodal learning, generative molecular design, explainable AI, molecular modeling, or the integration of chemical and biological data would be an asset.

Candidates are not expected to cover all of these areas. Scientific excellence, relevance to drug discovery, and complementarity with the existing expertise at CERMN will be the main considerations.

Specific Requirements

Scientific excellence, relevance to drug discovery, and complementarity with the existing expertise at CERMN will be the main considerations.

Applicants should have an internationally recognized scientific profile or demonstrate strong potential for international scientific leadership.

Languages: ENGLISH

Level: Excellent

Additional Information

Eligibility criteria

The call is intended for senior researchers meeting the eligibility requirements of the Normandy Region’s Chair of Excellence program, including researchers currently based outside Normandy.

Applicants should have an internationally recognized scientific profile or demonstrate strong potential for international scientific leadership.

Selection process

Applications should be submitted in English and should include:

1. a detailed curriculum vitae;
2. a complete list of publications;
3. a selection of up to five publications considered most representative of the applicant’s contribution to the field;
4. a research statement describing the applicant’s scientific achievements and how their expertise could contribute to the objectives of MOSAIC-Health;
5. a short statement describing proposed developments for the Chair, including potential research directions and their integration within the CERMN environment.

Applications must be emailed to alban.lepailleur@unicaen.fr

Additional comments

Employment conditions

The Chair is planned for a period of 42 months.

The final employment conditions will be established by Université de Caen Normandie in accordance with the applicable administrative and regulatory framework.

Website for additional job details: https://cermn.unicaen.fr/

Work Location(s)

Number of offers available: 1

Company/Institute: Université de Caen Normandie - CERMN (Centre d’Études et de Recherche sur le Médicament de Normandie) research centre (UR4258)

Country: France

City: Caen

Postal Code: 14032

Street: 1 Boulevard Becquerel

Contact

City: Caen

Website: http://www.unicaen.fr

Street: Esplanade de la Paix 14032 CAEN CEDEX

Postal Code: CS 14032

E-Mail: alban.lepailleur@unicaen.fr