Postdoctoral Researcher

Department:
Anatomy & Regenerative Medicine/CVRI Dublin
Reporting to:
Prof Osama Soliman, Deputy Director, CVRI Dublin
Location:
Mater Private Network/RCSI
Contract/Duration:
Specified Purpose, up to 12 months
Remuneration:
Aligned with the IUA Researchers RA salary, dependent on experience
Closing date:
Apply before 23:45 on 14th September
Summary
This role is based at the Cardiovascular Research Institute (CVRI) in Dublin, Mater Private Hospital in Dublin, and RCSI in Dublin.
The successful candidate will lead HRB-funded research to develop and validate machine-learning models that predict cardiotoxicity and breast cancer outcomes.
The role requires expertise in epidemiology, statistical modelling and artificial intelligence, as well as close collaboration with clinicians, imaging specialists, patient and public involvement (PPI) partners and the wider research team.
There will be opportunities to publish in high-impact journals and to supervise junior researchers.
This position is embedded within a highly interdisciplinary team of clinicians, physiologists, and engineers, and will be central to the development of patient-specific risk models and digital twin technologies aimed at optimising cardio-oncology patients\' outcomes.
Specifically, the duties of the post are:
The Clinical Research Assistant will work as part of Prof Soliman's Lab at RCSI, the CVRI Dublin, and the Mater Private Hospital.
Key Responsibilities
Conceptualise and design new studies investigating risk factors, trends and interventions for cancertherapy-related cardiovascular dysfunction (CTRCD), aligning with WP1 (Defining Prevalence and Patterns of CTRCD in Breast Cancer) and WP3 (Development of a ML based risk model using the integrated clinical and imaging dataset) of the grant.
Assemble and clean large clinical, imaging and biomarker datasets and secure appropriate access approvals.
Develop and implement advanced statistical and machine-learning algorithms (e.g., ensemble models, clustering) for risk prediction and patient segmentation.
Integrate clinical and echocardiographic features; perform hyperparameter tuning, cross-validation and external validation using state-of-the-art methods.
Evaluate model performance (AUROC, sensitivity, specificity, PPV/NPV); select interpretable models for clinical use.
Prepare documentation, reproducible code and web-based tools to facilitate translation of the risk models into clinical practice.
Lead and co-author manuscripts, presentations, policy briefs and grant applications; disseminate findings at national and international meetings.
Assist with ethics submissions and data governance compliance.
Qualifications – Essential
PhD (or equivalent) in epidemiology, biostatistics, computer science, biomedical engineering or a related discipline.
Demonstrated capacity for independent research and research leadership.
Strong peer-reviewed publication records relevant to the project.
Excellent quantitative analysis skills in epidemiology/biostatistics and machine learning; proficiency in programming (e.g., R, Python).
Experience working with large health datasets and familiarity with data-protection regulations.
Ability to work independently and to communicate clearly with multidisciplinary teams.
Knowledge and Experience – Desirable
Experience in cardio-oncology, cardiology or oncology research.
Familiarity with cardiac-imaging metrics and integration of imaging data.
Experience in patient-centred research and stakeholder engagement.
Track record of securing research funding and supervising students.
Experience in regulatory science, clinical trial coordination, or medical device development.
Knowledge of ISO 14155, MDR 2017/745, or FDA IDE pathway.
Experience in cardiovascular imaging or animal models of heart failure.
Familiarity with DICOM, in silico modelling tools, or core lab image analysis is a plus.
Ability to manage timelines and contribute to multi-stakeholder coordination.
We are committed to an inclusive, diverse environment and welcome applications from all qualified candidates.
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