Summary
Conor Lawless is a Scientific Computing Consultant with 13 years of experience applying mathematical and computational methods to complex biological and environmental systems. He combines expertise in dynamic model development, stochastic and hierarchical parameter inference, and large-scale automated image capture/analysis to extract biological insight from high-throughput experiments. Having built architectures to archive and interrogate tens of millions of robotically captured images for genome-wide Quantitative Fitness Analysis, he is comfortable designing end-to-end computational workflows that span C++, Python and R. His background bridges crop and plant-environment modelling through to cell- and tissue-level ageing research, and he thrives in strongly multidisciplinary teams that integrate experimental design with simulation and visualization. Based in Scotland, Conor is available for consultancy in mathematical and scientific computing, data science and interactive data visualization. An understated strength is his track record of turning bespoke experimental pipelines into reproducible, scalable compute platforms that unlock new biology.
13 years of coding experience
21 years of employment as a software developer
Master of Advanced Study, Mathematics (applied), Master of Advanced Study, Mathematics (applied) at University of Cambridge
PhD, Crop simulation modelling, PhD, Crop simulation modelling at University of Reading
Master of Engineering (MEng), Chemical Engineering, Master of Engineering (MEng), Chemical Engineering at Imperial College London
High School, High School at Glenstal Abbey School
French, Scottish Gaelic