Development of artificial intelligence-based predictive models for individual risk stratification and the simulation of disease progression trajectories in lung cancer screening programs

Italian Ministry of Education, University and Research; Italia; In loco; EUR 42.000 - 64.000; Tempo pieno

Descrizione del lavoro

ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA is seeking a researcher to develop, validate and interpret predictive models for individual risk stratification within the Try-A-Lung WP4 project.

The role involves designing post-test models that integrate clinical data and CT-derived features, applying ML/DL methods to estimate short- and long-term outcomes, and using Explainable AI to analyse trajectories and progression profiles.

Mansioni

• Develop, validate and interpret predictive models for individual risk stratification in Try-A-Lung WP4.
• Design post-test models to integrate clinical data and CT-derived features.
• Apply ML/DL methods to estimate short- and long-term outcomes.
• Use Explainable AI to analyze trajectories and progression profiles.
• Co-supervise theses and participate in seminars and conferences.

Descrizione del lavoro

Organisation/Company ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI INGEGNERIA DELL'ENERGIA ELETTRICA E DELL'INFORMAZIONE "GUGLIELMO MARCONI" Research Field Engineering » Biomedical engineering Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 12 Oct 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 activity focuses on the development, validation and interpretation of predictive models for individual risk stratification within WP4 of the Try-A-Lung project. A post-test model will be designed to integrate clinical data and quantitative features extracted from low-dose CT images, including nodule morphology, emphysema and risk scores for lung cancer, cardiovascular and respiratory diseases. Machine learning and deep learning models will estimate short- and long-term outcomes and will be evaluated using discrimination and calibration metrics, with comparisons against established models and advanced artificial intelligence methods. Explainable AI techniques will be used to analyse individual trajectories and identify clinically relevant progression profiles. The activities will also include the co-supervision of theses and participation in seminars and conferences.

• Italy

Eligibility of fellows: country/ies of residence:

• All

Eligibility of fellows: nationality/ies: