The post is within the SHIELD Joint Action (Strategies for Health Interventions to Eliminate Infection-related Cancers). The overall aim of SHIELD is to reduce premature morbidity and mortality caused by infections that lead to cancer, including human papillomavirus (HPV), hepatitis B and C viruses (HBV, HCV), human immunodeficiency virus (HIV), and tuberculosis (TB), through the development and implementation of coordinated, evidence-based prevention strategies across European countries.
This post will contribute specifically to the multi-disease modelling framework, which aims to generate integrated, policy-relevant evidence to inform the optimal design and prioritisation of prevention programmes.
The post is funded under the EU4Health programme, and the post-holder will work within a multidisciplinary and international consortium of academic institutions, public health agencies, and policy stakeholders. The modelling work will play a central role in supporting SHIELD’s objective to identify effective combinations of interventions, address barriers to implementation (including stigma and health system limitations), and translate analytical outputs into actionable recommendations for cancer prevention policy and practice.
The post-holder will lead the development and delivery of the multi-disease modelling work within SHIELD, contributing as a key member of a large, multidisciplinary and international consortium, providing technical guidance and mentoring to junior researchers where appropriate.
The primary objective is to design, implement, and operationalise a flexible, integrated modelling framework that allows disease-specific models (. HPV, HBV, HCV) to be incorporated within a common structure. This framework will enable coordinated, cross-disease analyses to support more holistic evaluation of cancer prevention and treatment strategies across EU Member States. The post-holder will work closely with partner modelling groups, supporting integration of their models and generating high-quality, policy-relevant evidence.
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