XenOmics – An Innovative Risk Assessment Toolkit Based on Omics Methodologies and Artificial Intelligence for Occupational Safety

The research aims to study Machine Learning (ML) strategies for the integration of omics and environmental data, the identification of potential biomarkers, and the development of a predictive model for toxicological risk

Selection process

The interview will center on verifying the candidate's competencies in statistical data analysis and machine learning, alongside scientific programming in Python. - Final degree grade (up to 10 points)- Publications: up to 5 points- Other qualifications related to the activity performed, such as research/work contracts, scholarships or fellowships, and official assignments: up to 25 points- Interview: up to 40 points

Job posting website Original MUR listing

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