法國洛林大學2024年招聘博士后（時空異構數據分析的統計和張量方法）
洛林大學（Université de Lorraine）是法國著名的公立綜合性大學之一，于2012年1月1日由三所同類型的公立綜合性大學和一所專業性較強的工程師學校重組合并而成: 南錫一大（ Université Henri Poincaré-Nancy 1），南錫二大（Université de Nancy 2），梅斯大學（Université Paul Verlaine - Universite de Metz），國立洛林綜合理工學院（INP Lorraine： Institut national polytechnique de Lorraine）。
(Postdoc offer) Statistical and Tensor Methods for Spatiotemporal Heterogeneous Data Analysis
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
為防止簡歷投遞丟失請抄送一份至：boshijob@126.com（郵件標題格式：應聘職位名稱+姓名+學歷+專業+中國博士人才網）
中國-博士人才網發布
聲明提示：凡本網注明“來源：XXX”的文/圖等稿件，本網轉載出于傳遞更多信息及方便產業探討之目的，并不意味著本站贊同其觀點或證實其內容的真實性，文章內容僅供參考。
