Annonce
PhD position on tensor-based unsupervised learning methods for neuroimaging data fusion
10 Février 2023
Catégorie : Doctorant
We are offering a PhD position on tensor-based unsupervised learning methods for neuroimaging data fusion, starting october 2023. The candidate will work primarily in the CRAN laboratory (University of Lorraine) at Nancy, France, with visits to the MLSP laboratory (UMBC) in Maryland, USA. She/he will work with Prof. David Brie and Dr. Ricardo Borsoi in the CRAN laboratory, Nancy, and with Prof. Tülay Adali at the MLSP laboratory, UMBC, UDA.
For further information, see:
http://ricardoborsoi.github.io/Positions/P_these_tensCRAN_UMBC_2023.pdf
We are offering a PhD position on tensor-based unsupervised learning methods for neuroimaging data fusion.
Location: Primarily the CRAN laboratory (University of Lorraine) at Nancy, France, with visits to the MLSP laboratory (UMBC) in Maryland, USA.
The candidate will work with Prof. David Brie and Dr. Ricardo Borsoi in the CRAN laboratory, Nancy, and with Prof. Tülay Adali at the MLSP laboratory, UMBC, UDA.
Starting date: October 2023
Description: Given the recent explosion in the amount of data from multiple modalities, a fundamental problem is fusing heterogeneous datasets containing dataset-specific information. The objective of the project is to develop flexible and directly interpretable unsupervised learning algorithms based on tensor decompositions for neuroimaging data fusion. Specific objectives include the study of their theoretical properties and demonstrate their performance on multi-subject and multimodality neuroimaging data for personalized medicine applications.
Candidate profile: Master’s degree or equivalent, with experience in signal processing, machine learning or applied mathematics.
To apply: If interested, please send your application including an academic CV and a motivation letter to david.brie at univ-lorraine.fr, ricardo.borsoi at univ-lorraine.fr and adali at umbc.edu.
For further information, see: http://ricardoborsoi.github.io/Positions/P_these_tensCRAN_UMBC_2023.pdf