Data Science and Biostatistics are at the core of modern health research. We advance evidence-based medicine by developing and applying innovative statistical methods, causal inference, machine learning, AI, and computational modelling to improve healthcare and patient outcomes.
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We support high-quality research across the full study lifecycle, from study design and data collection to analysis, interpretation, and implementation. Our teams collaborate closely with clinicians, researchers, medicines regulators, pharmaceutical companies and policymakers to design robust clinical trials, develop open science tools for FAIR data engineering and analyze complex trial, cohort and real-world health data.
Our expertise spans a wide range of data types and research settings, including clinical trial data, cohorts, real-world health records, high-dimensional data, unstructured textual data, and synthetic data. We address key methodological and practical challenges such as missing data, large-scale and heterogeneous datasets, data quality and governance, and work within diverse research infrastructures and tools, including common data models, federated analyses and AI.
In addition to research and innovation, we are strongly committed to education and training, empowering the next generation of researchers and healthcare professionals with the methodological and data-analytical skills needed to ask better questions and generate answers that matter.
Together, we enable data-driven innovation that supports rigorous science, personalized healthcare, and the future of medicine. We strive to enabling researchers to ask better questions and find answers that matter.