Université de Lorraine

(Postdoc offer) Statistical and Tensor Methods for Spatiotemporal Heterogeneous Data Analysis

2024-12-31 (Europe/Paris)
Save job

Offer Description

We are offering a postdoc position on the development of statistical and tensor decomposition methods for representation learning of heterogeneous data with application to the analysis neuroimaging data.

Location: 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. Sebastian Miron, Dr. Ricardo Borsoi and Prof. David Brie in the CRAN laboratory, Nancy, and with Prof. Tülay Adali at the MLSP laboratory, UMBC, USA.

The starting date is flexible (the position is open until filled).

Description: The analysis of spatiotemporal data is a fundamental problem in multiple domains such as neuroscience, epidemiology, climate science and pollution monitoring. Developing representation learning methods for spatiotemporal data that can effectively and jointly handle data from diverse modalities poses a significant challenge. A particular difficulty is to devise flexible models which are directly interpretable, readily providing insight into the relationships that are learned from the data. The candidate will develop flexible representations learning and data analysis methods specifically designed to handle heterogeneous spatiotemporal data, effectively utilizing both algebraic (matrix and tensor decompositions) and statistical frameworks to generate results that are interpretable and backed by statistical guarantees. The developed methods will be applied to personalized medicine, with the aim to elucidate the interplay between neuroimaging data (e.g., fMRI) and cognitive/socioeconomic factors as well as their temporal evolution.

Candidate profile: Ph.D. degree in signal processing, machine learning or applied mathematics or related fields.

To apply: If interested, please send your application including an academic CV and a 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, please see: https://cran-simul.github.io/assets/jobs/P_postdoc_these_NSF_2024.pdf


Research Field

Engineering » Electrical engineering

Education Level

PhD or equivalent





Internal Application form(s) needed

Apply now

Fill out the form below to apply for this position.
Allowed file types: PDF, DOC, DOCX, TXT, RTF
Allowed file types: PDF, DOC, DOCX, TXT, RTF

*By applying for a job listed on Academic Positions you agree to our terms and conditions and privacy policy.

By submitting this application, you consent to us retaining your personal data for service-related purposes. We value your privacy and will handle your information securely. Should you wish for your data to be removed, please contact us directly.

Job details

(Postdoc offer) Statistical and Tensor Methods for Spatiotemporal Heterogeneous Data Analysis
34 Cours Léopold Nancy, France
Application deadline
2024-12-31 23:59 (Europe/Paris)
2024-12-31 23:59 (CET)
Job type
Save job

About the employer

Université de Lorraine promotes innovation through the dialogue of knowledge, taking advantage of the variety and strength of its scientific fields...

Visit the employer page

This might interest you

Deciphering the Gut’s Clues to Our Health University of Turku 5 min read
Understanding Users to Optimise 3D Experiences Centrum Wiskunde & Informatica (CWI) 5 min read
Control Systems: The Key to Our Automated Future? Max Planck Institute for Software Systems (MPI-SWS) 5 min read
More stories