August Weinbren is a computational scientist with a background in computational physics, machine learning, and applied numerical methods. His research centres on quantifying the internal and extracellular forces involved in organoid morphogenesis using physics-informed machine learning approaches, including physics-informed neural networks (PINNs). In developing data-driven and physics-constrained models for tissue-scale dynamics, he works at the intersection of computational biology, tissue mechanics, and artificial intelligence.
Research themes
Technology
Computational Physics
High Performance Machine Learning
Physical Simulation
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C++
Python
Biography
2025-2029 Doctor of Philosophy at UCL in Computational Biophysics
2023-2025 Research software engineer, teaching and lab manager at UCL
2024-2025 Machine learning Engineer at Avian
2024-2024 Machine learning Engineer at Siemens Healthineers
​2023-2025 Research software engineer at UCL
2022-2022 Master of Science at UCL on Connected Enviroments
2020-2021 Scientific Software Engineer at the Met Office
2019-2020 Research assistant at The Johns Hopkins University
2015-2019 Bachelor of Science (Hons) at The Johns Hopkins University in Enviromental Engineering, Applied mathematics and statistics
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