Precision and adaptive radiotherapy in thoracic malignancies: integration of quantitative imaging, radiomics and artificial intelligence for treatment personalization

Italian Ministry of Education, University and Research; Italia; On-site; EUR 45,000 - 65,000; Full time

Job summary

Università Campus Bio-Medico di Roma in Italy invites applications for a Researcher Profile Leading Researcher (R4) in radiotherapy. The role focuses on precision and adaptive radiotherapy for thoracic malignancies, with emphasis on locally advanced NSCLC and oligometastatic settings, advancing translational clinical research within a multidisciplinary unit.

The successful candidate will identify and validate quantitative imaging biomarkers, integrate imaging with clinical and molecular data to

Responsibilities

• Identification and validation of quantitative biomarkers from imaging such as CT, PET/CT and functional imaging.
• Integration of imaging data with clinical, pathological, molecular and dosimetric information to develop predictive models of response, control and toxicity.
• Development and validation of machine learning, deep learning and explainable AI models with focus on robustness and clinical interpretability.
• Development and evaluation of adaptive radiotherapy strategies guided by anatomical, functional and biological changes during treatment.
• Investigation of combining radiotherapy with immunotherapy and targeted therapies to tailor strategies across thoracic disease settings.
• Methodologies to optimize patient selection, dose and treatment volumes to maximize therapeutic ratio and minimize toxicity.

Job description

Organisation/Company Università Campus Bio-Medico di Roma Research Field Medical sciences » Medicine Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 3 Dec 2026 - 12:00 (UTC) Country Italy Type of Contract Temporary Job Status Full-time Hours Per Week 350 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

Radiotherapy for thoracic malignancies is evolving toward increasingly personalized models based on the integration of clinical, biological, molecular, dosimetric, and quantitative imaging information. Artificial intelligence represents a further element of innovation, enabling the identification of predictive biomarkers, supporting patient selection, and facilitating dynamic and adaptive treatment strategies. The research program will focus on the development, validation, and clinical translation of precision and adaptive radiotherapy approaches in lung cancer, with particular emphasis on locally advanced NSCLC and oligometastatic and oligoprogressive settings, while maintaining applicability to the major thoracic malignancies.

• identification and validation of quantitative biomarkers derived from CT, PET/CT and, where available, functional imaging, using radiomics and longitudinal analyses;
• integration of imaging data with clinical, pathological, molecular, and dosimetric information to develop predictive models of response, disease control, toxicity, and survival;
• development and validation of machine learning, deep learning, and explainable artificial intelligence (XAI) models, with particular attention to robustness, reproducibility, and clinical interpretability;
• development and evaluation of adaptive radiotherapy strategies guided by anatomical, functional, and biological changes observed during treatment;
• investigation of the integration of radiotherapy, immunotherapy, and molecularly targeted therapies, identifying personalized strategies across different thoracic disease settings;
• development of methodologies to optimize patient selection, dose, and treatment volumes, maximizing the therapeutic ratio and reducing toxicity.

The program will be integrated into the multidisciplinary activities of the Radiation Oncology Research Unit, fostering collaboration with radiology, nuclear medicine, pathology, medical oncology, thoracic surgery, and engineering and computer science disciplines. Prospective and retrospective collections of clinical and biological data will be developed, with internal and external validation of models and national and international multicenter collaborations to assess their robustness and clinical transferability. The ultimate goal will be to translate research findings into clinical protocols, quantitative biomarkers, and decision-support tools, contributing to personalized, data-driven radiotherapy. The program aims to improve therapeutic efficacy, identify patients most likely to benefit, and reduce toxicity, strengthening the role of translational and multidisciplinary research in advancing radiation oncology.