Bernd Taschler is a Senior Principal Biostatistician with 11 years of experience translating theoretical physics and complexity science into pragmatic statistical solutions for neuroimaging and neurodegenerative disease research. Trained with a PhD in Statistics & Complexity Science from Warwick, he has driven Bayesian, causal inference, and high-dimensional population modelling across academic labs and industry, including the Novartis–Oxford AI in medicine collaboration. His work blends spatial point process modelling for MRI with scalable computational statistics and machine learning, and he has a track record of moving methods from postdoc research settings into pharmaceutical quantitative science. Based in Oxford, he pairs deep theoretical rigor with practical data-science engineering, often tackling measurement- and population-level challenges that are easy to overlook in standard pipelines. Colleagues value his cross-disciplinary fluency—physics, philosophy and computation—which helps him see unconventional model structures and causal signals in noisy clinical data.
11 years of coding experience
9 years of employment as a software developer
Theoretical and Computational Physics, Theoretical and Computational Physics at Inha University 인하대학교
Master of Science (MSc) / DI, Theoretical and Computational Physics, Master of Science (MSc) / DI, Theoretical and Computational Physics at Technische Universität Graz
Doctor of Philosophy (PhD), Statistics & Complexity Science, Doctor of Philosophy (PhD), Statistics & Complexity Science at University of Warwick
Contributions:9 pushes, 1 branch in 1 year 7 months
spatialbayesian-inferencebayesianlinear
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