Associate Director, Systems Medicine

Location: The Discovery Center (DISC), Cambridge Biomedical Campus, UK Salary: Competitive Salary and Benefits! Introduction to the role: Are you ready to turn mechanistic models into dose and schedule strategies that protect patients and accelerate development? Join a team of specialist modelers who operate with high visibility and real decision-making influence, shaping clinical strategy across therapy areas. Based at our Discovery Centre in Cambridge, UK, you will work in a dynamic, multidisciplinary environment spanning nonclinical and clinical phases. About the role: The Systems Medicine group is seeking a Systems Modeler passionate about using mathematical and computational skills to develop and apply empirical and/or mechanistic models of Pharmacology and Toxicology. The group is under Clinical Pharmacology & Quantitative Pharmacology Department and consists of ~20 mathematical modelers with backgrounds in applied biomathematics, computational biology, and/or biomedical/chemical engineering. Working in a dynamic, multidisciplinary environment the successful candidate support projects in both non-clinical and clinical phases. The candidate will develop and apply pharmacological mechanistic systems models to contribute to decisions on dose regimens by balancing efficacy and safety via modelling & simulation based on the understanding of the mechanism of action of investigational drugs. The role will include opportunities to develop and apply Quantitative Systems Pharmacology (QSP) and Toxicology (QST) models, including incorporation of virtual populations to support translational decision-making and dose/schedule selection. In addition, the incumbent will develop QSP&T models based on Microphysiological Systems (organ-on-chips and organoids) . To succeed in this role, we believe you have drug development experience and you are a person who enjoys working collaboratively with a variety of key stakeholders and collaborators to identify opportunities, build support and deliver innovative modelling and simulation solutions. Experience or exposure in modalities such as immune cell engagers, antibody-drug conjugates (ADCs), and radioconjugates (RCs) would be valuable. Main responsibilities : Create, expand or refine mathematical models to address drug-discovery and nonclinical/clinical development questions Lead compound-specific projects with hands-on analysis by choosing the best modelling approach to address questions Contribute to the design, execution, and interpretation of clinical studies Develop and apply clinical QSP &T models, including virtual population approaches, to support prediction of efficacy, safety, and dose regimens in clinical development Test and adopt existing modelling platforms Review modelling works by colleagues, ensuring high-quality standards Contribute to AZ drug development with innovative ideas Stay informed with emerging literature and science in modelling and simulation sciences, including developments in clinical QSP &T models , virtual populations, and digital twin approaches Collaborate well within the Systems Medicine group and cross-functional teams Guide junior modelers Represent AZ by publication, podium presentations, and/or organization of symposia Essential requirements: PhD or similar degree in chemical, mechanical or biomedical engineering, physics, applied mathematics or related field Experience working in the industry and postdoctoral experience in building, validating , and using predictive mechanistic mathematical models for drug development. (Ideally, 4 years of experience). At least 3 published papers Excellent understanding of theory, principles and statistical aspects of mathematical modelling and simulation, including numerical methods, parametrization and ODEs. Knowledge of models of biological pathways/systems to support translational research. Hands-on knowledge of modelling with ODEs, Agent-Based Modelling, Statistical and/or Machine Learning modelling, etc Aptitude and experience to influence decisions and experimental design by using available data and appropriate modelling solutions Self-directed, independent, and highly-motivated researcher who excels in a collaborative, multi-disciplinary environment. Evidence of identifying, developing, and applying innovative solutions to scientific and technological problems faced in systems and predictive modelling Familiarity with the challenges of drug discovery and forward thinking with respect to the general application of mathematical models in drug discovery and development Excellent oral and written communication skills and the ability to interact effectively with scientists in other subject areas with a positive and collaborative attitude Experience with data analysis tools and languages such as Matlab and/or Python. <u