This PDF position is focused on ML in realistic settings referring to statistical and system characteristics in the real world contrary to an ideal learing setting. The candidate will contribute to understanding how neural networks extract the most relevant information of the data to make a prediction using advanced mathematical tools. This insight opens the door for enjoying the real world. The candidate further develops efficient and robust algorithms for realistic settings in terms of data and computing resources and collaborates to address major challenges in important applications including marine domain and neuroscience. The candidate is expected to assist in supervising students at both master's and PhD levels.

This project is funded by a prestigious FRIPRO Grant for Early Career Scientists and is built on our ongoing Integreat project “Embedded Sufficient Statistics”.

This position is placed at Integreat - Norwegian Centre for Knowledge-driven Machine Learning is a Centre of Excellence, funded by the Research Council of Norway. Researchers at Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By combining the mathematical and computational cultures, and the methodologies of statistics, logic and machine learning in unique ways, Integreat's machine learning will solve fundamental problems in science, technology, health and society, and contribute to make Norway a world leading country in AI.

For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/283109/postdoctoral-research-fellow-in-machine-learning