XenOmics – An Innovative Risk Assessment Toolkit Based on Omics Methodologies and Artificial Intelligence for Occupational Safety
Organisation/Company Dipartimento di Scienze biochimiche Alessandro Rossi Fanelli Research Field Biological sciences » Biology Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 15 Nov 2026 - 23:59 (UTC) Country Italy Type of Contract To be defined Job Status Not Applicable Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No
Offer Description
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
- 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
- Italy
Eligibility of fellows: country/ies of residence:
- EUROPE
Eligibility of fellows: nationality/ies:
- EUROPE
Selection process
The interview will center on verifying the candidate's competencies in statistical data analysis and machine learning, alongside scientific programming in Python.
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Offer Description
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
- 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
- Italy
Eligibility of fellows: country/ies of residence:
- EUROPE
Eligibility of fellows: nationality/ies:
- EUROPE
Selection process
The interview will center on verifying the candidate's competencies in statistical data analysis and machine learning, alongside scientific programming in Python.
#J-18808-Ljbffr