Copenhagen, Capital Region of Denmark, Denmark
MSc. student with a demonstrated history of working with simulations and machine learning in the field of batteries. Skilled in Data analysis, and Physics. Strong team player, always looking to learn and improve solutions to different problems. Currently pursuing an MSc. in Physics and nanotechnology at DTU (Technical University of Denmark) Graduated July 2023
Machine learning driven autonomous discovery of electrocatalysts for H2 and O2 evolution in alkaline environment
-Developing predictive models for the conductivity of battery electrolytes -Aggregated and standardized +14 000 datapoints from +30 sources to build foundations of an open-source database -Benchmark performance of predictive models -Analyzed data to determine outliers, and quantify variance between different sources
Teaching assistant for first, and second year students at DTU-Physics. Courses included: Physics 1 Mechanics and physical modelling
Part of 1 month internship (Helmholtz institute + EPFL), for BIG-MAP project Tasks included -Developed pipeline for sending data from autonomous experiments to SQLite database, allowing easy access to data for research institutions -Utilized automated and manual methods for assessing code
-Implement model for battery cell-lifetime prediction