Postdoctoral Researcher Rinn Artificial Intelligence – Research & Innovation in Data Science and AI

Descrizione

Postdoctoral Researcher Rinn Artificial Intelligence – Research & Innovation in Data Science and AI Sintesi in italiano (traduzione automatica): Si cercano candidature per una posizione di Ricercatore post-dottorato all'interno del progetto Rinn Artificial Intelligence – Ricerca e Innovazione in Data Science e AI, finanziato da Research Ireland, presso l'Università College Cork. Il ruolo è ospitato dalla Scuola di Scienze Matematiche e prevede la supervisione del Dr. Eric Wolsztynski. Le principali mansioni includono lo sviluppo di metodi teorici e applicati in modellazione statistica, analisi di dati di imaging medico e medicina di precisione. I candidati dovranno avere esperienza in machine learning, in particolare in radiomica e trascrittomica, e lavoreranno su progetti come la previsione del rischio per pazienti oncologici e l'analisi di dati di imaging. È richiesta una laurea in discipline affini, come Matematica o Informatica. Applications are invited for a Post-Doctoral research position within the Rinn Artificial Intelligence – Research & Innovation in Data Science and AI funded by Research Ireland within the School of Computer Science and Information Technology and this position will be hosted by the School of Mathematical Sciences in UCC. This role is part of a national Centre, Rinn Artificial Intelligence – Research & Innovation in Data Science and AI, a multi-institutional interdisciplinary National Centre for Data Science and AI funded by Research Ireland. University College Cork is a Co-lead and this position for Post-Doctoral researcher will sit within the School of Mathematical Sciences under the supervision of Research Ireland Principle Investigator, Dr Eric Wolsztynski. The host research group leads a number of projects on the development of theoretical and applied methods in statistical modelling, medical imaging data analysis, cancer omics, precision medicine and translational statistics. Depending on their profile, the successful candidate will work on one of the following strands. • Bespoke modelling of tumour metabolic function using 3D and 4D imaging data • Cancer patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics) • Multi-omics for non-cancer health screening applications, • Machine learning modelling for mediation analysis and for longitudinal studies This transdisciplinary research stems from a number of collaborations with clinical partners, using clinical and observational data, with biomarker discovery and clinical characterization among the main objectives. One key aim of the group is to further the development of bespoke solutions for cancer imaging based on spatial analysis and kinetic modelling techniques. This strand would involve adapting in-house models for Positron Emission Tomography data to other imaging modalities. Research in the other strands that focus on machine learning applications will aim to leverage and adapt appropriate strategies for fairness evaluation and explainability in wide-data risk prediction modelling. Annuncio in inglese. Fonte: Euraxess (Commissione europea).