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Offer Description
We are looking for a talented, research-oriented PhD candidate to join the project “Curve Ensemble Gradient Descents for Sparse Dynamic Problems”.
Optimization methods in the space of measures have seen a dramatic increase in popularity in recent years. This is largely due to two factors: their connections with Optimal Transport (OT) and Wasserstein Gradient Flows, and their applicability to solve inverse problems and design infinite-dimensional machine learning algorithms.
The goal of this project is the analysis of dynamic optimization problems for time-dependent measures, where motion is governed by the penalization of OT energies. We plan to adopt a sparse perspective, analyzing how collections of trajectories can be employed to approximate dynamic distributions and how gradient descent methods in the space of curves can be used to achieve this goal. A full understanding of these dynamic problems and their efficient optimization is essential to address a wide class of inverse problems and machine learning methods. In particular, applications to single-particle tracking (SPT) for fluorescence microscopy and neuralODEs training will be considered.
The PhD candidate will work under the supervision of Dr. Marcello Carioni and will be part of the group “Mathematics of Imaging and Artificial Intelligence” (MIA) at the Department of Applied Mathematics. There will be plenty of opportunities for collaboration with researchers in the group of Prof. Carola Schönlieb at the University of Cambridge and in the group of Prof. Kristian Bredies at KFU Graz.
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Are you interested in this position? Please send your application via the ‘Apply now’ button below before 5th of April 2024, and include:
We particularly encourage/support female applicants to apply.
For more information regarding this position, you are welcome to contact Dr. Marcello Carioni ([email protected] )
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STATUS: EXPIRED
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