AI & Machine Learning for Precision Treatment of Paediatric Brain Cancer (ADAPTS Program)

Company Description The School of Biomedical Sciences and Pharmacy at the University of Newcastle is a leading Australian provider of undergraduate and postgraduate education in biomedical science and pharmacy. The School hosts multidisciplinary research groups working across Immunology, Neuroscience, Oncology, Neurology, Cardiology, Anatomy, and Reproduction. Researchers collaborate closely with clinical partners and industry to translate discoveries into improved health outcomes. The School offers a supportive, research-intensive environment with access to modern laboratories, cutting-edge technologies, and mentoring. Team members benefit from a collaborative culture, professional development opportunities, and strong institutional support for impactful research. Role Description This full-time, on-site role in the Greater Newcastle Area focuses on developing and applying AI and machine learning methods for the precision treatment of pediatric brain cancer within the ADAPTS Program. Day-to-day responsibilities include designing and implementing machine learning and deep learning models, curating and preprocessing clinical and biological datasets, and applying statistical methods to analyze complex multi-modal data. The role involves collaborating with clinicians, biologists, and data scientists to define research questions, interpret results, and translate findings into clinically meaningful insights. The successful candidate will document methods and results, prepare manuscripts and reports, contribute to grant applications, and present work at lab meetings, seminars, and conferences. Additional tasks may include maintaining reproducible codebases, adhering to data governance and ethical standards, and supporting junior team members in data and analysis workflows. Qualifications

• Strong foundation in Computer Science and Algorithms for designing efficient, scalable analytical and predictive solutions.
• Proficiency in Machine Learning and Deep Learning, including experience with frameworks such as TensorFlow, PyTorch, or similar.
• Solid understanding of Statistics for experimental design, model evaluation, uncertainty estimation, and data interpretation.
• Experience with scientific programming (e.g., Python, R, or MATLAB) and version control tools (e.g., Git) in a research or production environment.
• Relevant degree (e.g., Honours, Master’s, or PhD in Computer Science, Data Science, Biomedical Engineering, Mathematics, or related field) or equivalent experience.
• Ability to work effectively in interdisciplinary teams, communicate complex technical concepts to non-technical stakeholders, and document work clearly.
• Familiarity with biomedical or clinical data, especially oncology or neuroscience, and an interest in pediatric brain cancer research is highly desirable.
• Commitment to ethical handling of sensitive data, reproducible research practices, and continuous learning in AI, ML, and biomedical applications.