Miglė Graužinytė

Miglė Graužinytė has a diverse work experience in the field of data science and research. Miglė is currently working at Spiden as a Data Scientist, where they lead a project team focused on predictive model robustness using deep learning approaches. Prior to that, they worked at Novartis as a Machine Learning PostDoc, where they utilized supervised and unsupervised learning techniques to enhance drug product quality control. Miglė also developed an image-filter to automatically identify a harmless component. Before joining Novartis, Miglė worked as a Postdoctoral Researcher at the Department of Physics, University of Basel. In this role, they conducted high-throughput defect calculations to fine-tune material properties and made contributions to the discovery of a novel p-type transparent conductor. Miglė also explored the possibilities of defect-aided superconductivity under high pressure.

Miglė Graužinytė pursued their education in the field of physics. Miglė completed their undergraduate studies at The University of Edinburgh from 2009 to 2014, earning a Master's degree with Honours. Later, from 2015 to 2018, they attended the University of Basel, where they obtained a Doctor of Philosophy (PhD) degree in Computational Physics.