[Job-offers-cs] Postdoctoral position, Optimisation, AI in health - Lille University, january 1st, 2021

[Pekka Orponen]

-------- Forwarded Message --------
Subject: [DMANET] Postdoctoral position, Optimisation, AI in health - 
Lille University, january 1st, 2021
Date: Mon, 23 Nov 2020 10:58:12 +0100 (CET)
From: Clarisse Dhaenens <clarisse.dhaenens at univ-lille.fr>
To: dmanet <dmanet at zpr.uni-koeln.de>

Dear all,

Applications are invited for a 2-year postdoctoral fellow to begin on 
January 1, 2021. The position will be co-supervised by Pr. Clarisse 
Dhaenens, computer Science, Pr. Vincent Sobanski, internal medicine and 
Pr. Grégoire Ficheur, medical informatics

The postdoc is part of a research cluster from I-Site ULNE ( [ 
http://www.isite-ulne.fr/index.php/en/home/ | 
http://www.isite-ulne.fr/index.php/en/home/ ] ).

She/He will be hosted in the CRIStAL labs (computer science Lab - 
[https://www.cristal.univ-lille.fr/ | 
https://www.cristal.univ-lille.fr/] - Univ. Lille, CNRS, Centrale Lille) 
and is financed by Lille metropole (MEL - [ 
https://www.lillemetropole.fr/ | https://www.lillemetropole.fr/ ] )

She/He will also work actively with the INCLUDE team at the Lille 
University Hospital (health data warehouse project - [ 
https://include-project.chru-lille.fr/en/the-team/ | 
https://include-project.chru-lille.fr/en/the-team/ ] ), the METRICS team 
(ULR 2694, Lille University Hospital), and the INFINITE team (INSERM 
U1286, Lille University Hospital - [ 
http://lille-inflammation-research.org/en/workpackages/861-wp3-en | 
http://lille-inflammation-research.org/en/workpackages/861-wp3-en ] ).

The applicant should have a PhD in Computer Science. The ideal candidate 
would have expertise in one or more of the following areas:
Operations research, Combinatorial Optimization, Data Mining 
(clustering, bi-clustering), optimization approaches, textual analysis...
An interest or an experience about health domain will be appreciated.
The position is open to candidates of any nationality and selection will 
be based upon the candidate's research record and potential.

Application material will be submitted by e-mail to [ 
mailto:clarisse.dhaenens at univ-lille.fr | clarisse.dhaenens at univ-lille.fr 
] before December 1rst 2020 , and include (in a single PDF file)
- Cover letter
- CV
- 2 reference letters
- Research statement

Project objectives
The objective of this project is to use a biclustering approach to 
combine two different and complementary data sources in order to better 
characterize endotypes of chronic inflammatory diseases:
• Build a "word embedding" type representation based on BERT modeling 
(Bidirectional Encoder Representations from Transformers, Devlin 2018) 
from textual concepts present in medical letters from a computerized 
patient records database.
• Filter a set of characteristics unrelated to the type of diagnosis 
that may be found among the interests of patients.
• Build clusters by the biclustering method, based on (i) the identified 
signs, (ii) on the previously constructed embedding and / or (iii) on 
the variables from a local complete database.
• Compare the clusters obtained using these different approaches, in 
particular with regard to the concordance on the grouping of patients as 
well as on the clinical relevance of the subgroups identified.

Do not hesitate to contact us to have more details. A complete 
description of the projet can be sent if required. Sincerely,




---
Clarisse Dhaenens Vice présidente recherche : Sciences et Technologies 
Université de Lille
[ mailto:clarisse.dhaenens at univ-lille1.fr | 
clarisse.dhaenens-flipo at univ-lille.fr ] | [ 
http://cristal.univ-lille.fr/ | cristal.univ-lille.fr/~dhaenens ]
Siège : 42, rue Paul Duez - 59800 Lille


CRIStAL : Bureau S3.03 - Bât. ESPRIT - 59655 Villeneuve d'Ascq
Tél : +33 (0)3 28 77 85 82


!! Nouvelles publications !! A Biclustering Method for Heterogeneous and 
Temporal Medical Data [ 
https://ieeexplore.ieee.org/abstract/document/9055135 | 
https://ieeexplore.ieee.org/abstract/document/9055135 ]



The detection of hospitalized patients at risk of testing positive to 
multi-drug resistant bacteria using MOCA-I, a rule-based “white-box” 
classification algorithm for medical data [ 
https://www.sciencedirect.com/science/article/abs/pii/S1386505620301465?via%3Dihub 
| 
https://www.sciencedirect.com/science/article/abs/pii/S1386505620301465?via%3Dihub 
]