Location: The CRAN laboratory (University of Lorraine) at Nancy, France, with visits to the MLSP laboratory (UMBC) in Maryland, USA. The candidates will work with Prof. Sebastian Miron, Dr. Ricardo Borsoi and Prof. David Brie in CRAN, Nancy, and with Prof. Tülay Adali at the MLSP laboratory, UMBC, USA.
The starting date is flexible (the positions are open until filled).
Description: Low-rank matrix and tensor decompositions are fundamental tools in data analysis and fusion, as they take into account the multi-dimensional representation inherent to many real-world applications and readily provide insight into the relationships learned from the datasets. However, developing low-rank decomposition methods which are flexible to represent real-world datasets while at the same time retaining the strong theoretical guarantees is challenging. The general objectives of the project are to: 1) develop coupled tensor decompositions for data fusion which account for dataset-specific information, and 2) develop tensor decomposition methods for the analysis of heterogeneous data leveraging both algebraic (e.g., low-rank decompositions) and statistical frameworks. An important objective is to study the theoretical properties of the developed algorithms. The methods will be applied to multi-subject and multimodal neuroimaging data for personalized medicine applications.
Candidate profile:
- for the PhD position: Master's degree or equivalent, with experience in signal processing, machine learning or applied mathematics.
- for the postdoc position: Ph.D. degree in electrical engineering, applied mathematics or related fields.
To apply: If interested, please send your application including an academic CV and a short motivation letter to sebastian.miron@univ-lorraine.fr, ricardo.borsoi@univ-lorraine.fr, david.brie@univ-lorraine.fr, and adali@umbc.edu.
For further information on both positions, please see:
- For the PhD position: https://cran-simul.github.io/assets/jobs/Phd_these_LUE_2024.pdf
- For the PhD/postdoc position: https://cran-simul.github.io/assets/jobs/P_postdoc_these_NSF_2024.pdf
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