[Comp-neuro] Postdoc position on causal learning in France

Mehdi Khamassi mehdi.khamassi at upmc.fr
Fri May 17 08:36:45 CEST 2019


*Post-Doc position in Computational Neuroscience on causal learning *

*Institut des Systèmes Intelligents et de Robotique, Paris (UMR7222)*

*Groupe d'Analyse et de Théorie Economique, Lyon (UMR 5824)*

*General Information*

Workplace: LYON/PARIS

Employer: CNRS

Type of contract: FTC Scientist

Contract Period: 18 or 24 months

Expected date of employment: 2 September 2019

Proportion of work: Full time

Remuneration: between 2643 and 3766 Euros gross monthly salary depending 
on the candidate’s experience

Desired level of education: PhD

Experience required: indifferent

*Missions*:

The objective of our project is to investigate the ​neural and 
computational bases of causal learning​. In particular, we will focus on 
causal learning in the context of ​goal-directed instrumental behaviors, 
which rely on learning rules determined by the ​contingency//between 
actions and outcomes. In order to unravel the neural and computational 
bases ​of action-outcome causal learning, we need to lift two key 
barriers. The ​first barrier ​is the lack of neurocomputational models 
that formalise the above-mentioned theoretical framework to make precise 
predictions about the underlying neural computations. The ​second 
barrier ​is the lack of clear understanding of the brain network 
dynamics supporting action-outcome causal learning.

*Activities: *

The selected candidate will contribute to lift the first barrier, by 
developing neurocomputational models that: i) formalise internal 
representations and computations predicted by causal learning theories 
(​/rational /​and ​/Bayesian /frameworks); ii) make predictions about 
the dynamics of neural activity (i.e., neurobiological plausibility) and 
fit single-participant behavioral patterns (i.e., computational 
flexibility). Among the possible learning models, two seem to provide 
the adequate theoretical and computational framework: ​Active Inference​ 
and ​Reinforcement learning​. The former ​is a Bayesian approach 
postulating that human behaviour can be reduced to the minimization of 
variational free energy, which is an upper bound on Shannon surprise 
​(Friston, 2010)​. The latter provides a complementary view and 
formalizes human behavior as a process that aims at maximizing 
cumulative (extrinsic or intrinsic) reward (Sutton & Barto, 1998).

The selected candidate will help designing a new experimental protocol 
to test the specific predictions of these computational frameworks, and 
compare them with Bayesian model comparison methods. The experiment will 
involve human subjects and be realized with the facilities of the GATE 
(Experimental Economics) laboratory in Lyon, France. Then we will derive 
a computational model which best accounts for human behavior while they 
learn causalities, and make model-driven predictions for a new task 
involving brain imaging (fMRI, MEG, SEEG) performed in humans by 
partners of the project.

*Skills*:

Applicants should be highly motivated, have a PhD in computational 
neuroscience, physics, computer science, or related fields, with a track 
record of publications. Confirmed experience in computational modelling 
and programming skills are mandatory. Preference will be given to 
applicants with previous experience in causal learning models in 
neurosciences.

*Work context*:

We have obtained funding from the French Agence Nationale de la 
Recherche for 4 years project aiming at lifting the previously mentioned 
two barriers. The project involves both theoreticians (Mateus Joffily in 
GATE, Lyon, France; Mehdi Khamassi in ISIR, Paris, France; David Lagnado 
in UCL, London, UK) and experimentalists (Andrea Brovelli in INT, 
Marseille, France; Julien Bastin in GIN, Grenoble, France). This 2 years 
post-doctoral research position is available to work at the interface 
between two laboratories: the GATE in Lyon and the ISIR in Paris.

*Additional Information*:

The position is available immediately and applications will be reviewed 
until the position is filled. The selected candidate will be hired for a 
period of 18 to 24 months with a salary corresponding to the level of 
research scientist according to the standards of the National Center of 
Scientific research (CNRS). Salary will include social security, health 
and retirement benefits.

Applications should include: 1) a cover letter briefly describing 
experience, motivation and skills adapted for the position, as well as 
research interests; 2) complete CV and publication list; and 3) two 
letters of reference that should be sent directly to us by the evaluators.

Notification of interest should be sent to both Mehdi Khamasi 
(mehdi.khamassi at upmc.fr <mailto:mehdi.khamassi at upmc.fr>) and Mateus 
Joffily (joffily at gate.cnrs.fr <mailto:joffily at gate.cnrs.fr>).

Applications shall be done through the following website: 
https://emploi.cnrs.fr/Offres/CDD/UMR5824-TAIDAO-009/Default.aspx?lang=EN.

-- 
Mehdi Khamassi, PhD, HDR
Permanent research scientist (CRCN) at the Centre National de la Recherche Scientifique,
Institute of Intelligent Systems and Robotics
Sorbonne Université, Paris, France
http://www.isir.upmc.fr

Director of Studies for the Cogmaster program
Ecole Normale Supérieure, EHESS, Univ. Paris Descartes
http://sapience.dec.ens.fr/cogmaster/www/

Visiting Researcher at the Institute of Communication and Computer Systems
National Technical University of Athens, Greece
https://www.iccs.gr/en/?noredirect=en_US

Visiting Researcher at the Department of Experimental Psychology
University of Oxford, UK
https://www.psy.ox.ac.uk

Main contact details:
Mehdi Khamassi
Sorbonne Université, Campus Pierre et Marie Curie - ISIR - BC 173
4 place Jussieu, 75005 Paris, France
tel: + 33 1 44 27 28 85
cell: +33 6 50 76 44 92
email: mehdi.khamassi at upmc.fr
http://people.isir.upmc.fr/khamassi

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://www.tnb.ua.ac.be/pipermail/comp-neuro/attachments/20190517/1113fdc3/attachment.html>


More information about the Comp-neuro mailing list