Project description and tasks: The primary objective of this project is to address key challenges in functional data analysis, with a specific focus on statistical testing in the presence of misalignment. We aim to devise innovative methodologies rooted in optimization theory and machine learning algorithms. The focus is on creating autonomous, robust methods capable of performing intricate statistical analysis in the face of domain distortion. Importantly, these methods will prioritize computational efficiency, facilitating their use in large scale real-time data processing scenarios. In practical terms, our project is poised to deliver significant impact across a range of domains. In medical imaging, we aim to enhance both the accuracy and efficiency of disease detection algorithms by improving how they handle misalignment. In the biomechanical sphere, we hope to enable more precise tracking of movement, with potential benefit to areas such as sports performance and rehabilitation. Finally, for signal processing, the project has the potential to advance pattern identification in time-series data, notably in sensor data analysis and anomaly detection.

The project is part of the AI/MLX track of the Wallenberg AI, Autonomous Systems, and Software Program (WASP), and the PhD student will be enrolled in the WASP graduate school, see https://wasp-sweden.org/graduate-school/ for more information.

Umeå offers excellent working and living conditions. The city is young and located right next to a large river. It is surrounded by forests and lakes and lies close by the sea. In the vicinity there are plenty of opportunities for both indoor and outdoor activities.

For further information and instructions on how to apply, see:

https://umu.varbi.com/en/what:job/jobID:634615/