Connectionists: POSTDOC POSITION @ TUB in Risk-Sensitive Reinforcement Learning on Partially Observable Markov Decision Processes

[Youssef Kashef]

Technische Universität Berlin, Fakultät IV - Electrical Engineering
and Computer Science
POSTDOC POSITION
3 years, salary level: E-13 TV-L

RISK-SENSITIVE REINFORCEMENT LEARNING ON PARTIALLY OBSERVABLE MARKOV
DECISION PROCESSES

The successful candidate will join a DFG funded project whose goal is
to develop a mathematical framework for optimal sequential decision
making in the face of economic and perceptual uncertainty. The project
aims at the extension of Partially Observable Markov Decision
Processes to incorporate risk, at an assessment of the existence and
uniqueness of optimal risk-sensitive policies, and at the development
of computationally tractable algorithms for solving the corresponding
optimization problems. For previous work in the context of
risk-sensitive Markov Decision Processes see: Shen, Stannat, Obermayer
(2013), SIAM J. Control Optim. 51, 3652ff; Shen et al. (2014), Neural
Comput. 26, 1298ff; Shen, Stannat, Obermayer (2014), ArXiv e-prints
1403.3321.

Candidates should hold a recent PhD-degree in mathematics
(stochastics), computer science (machine learning), or similar fields.
Candidates with research experience in optimization and control will
be preferred.

Application material (CV, list of publications, abstract of the PhD
thesis, copies of certificates and two letters of reference) should be
sent to:

Prof. Dr. Klaus Obermayer
MAR 5-6, Technische Universität Berlin, Marchstrasse 23
10587 Berlin, Germany
http://www.ni.tu-berlin.de/
email: klaus.obermayer at tu-berlin.de

preferably by email.

All applications received before June 30th, 2017, will be given full
consideration, but applications will be accepted until the position is
filled.

TUB seeks to increase the proportion of women and particularly
encourages women to apply. Women will be preferred given equal
qualification. Disabled persons will be preferred given equal
qualification.