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<div style="border-style: none none double; border-bottom-color: windowtext; border-bottom-width: 2.25pt; padding: 0cm 0cm 1pt; position: static; z-index: auto;"><p style="border:none;mso-border-bottom-alt:double windowtext 2.25pt;
padding:0cm;mso-padding-alt:0cm 0cm 1.0pt 0cm"><b><span lang="EN-US" style="font-size:12.0pt;mso-bidi-font-size:10.0pt;
font-family:"Times New Roman"">Call for postdoc application:<o:p></o:p></span></b></p><p align="center" style="text-align:center;border:none;mso-border-bottom-alt:
double windowtext 2.25pt;padding:0cm;mso-padding-alt:0cm 0cm 1.0pt 0cm"><b><i><span lang="EN-US" style="font-size:14.0pt;mso-bidi-font-size:10.0pt;font-family:"Times New Roman"">Automated
discovery of physico-chemical structures with intrinsically motivated deep
learning algorithms</span></i></b><b><span lang="EN-US" style="font-size:12.0pt;mso-bidi-font-size:10.0pt;font-family:"Times New Roman""><br>
</span></b><b><span lang="EN-US" style="font-size:14.0pt;mso-bidi-font-size:12.0pt;font-family:"Times New Roman""><br>
</span></b><b><span lang="EN-US" style="font-size:12.0pt;mso-bidi-font-size:10.0pt;font-family:"Times New Roman"">Project
Team Inria</span></b><span lang="EN-US" style="font-size:12.0pt;mso-bidi-font-size:
10.0pt;font-family:"Times New Roman"">: Flowers, Inria Bordeaux, <a href="https://flowers.inria.fr">https://flowers.inria.fr</a> </span><b><span lang="EN-US" style="font-size:14.0pt;
mso-bidi-font-size:12.0pt;font-family:"Times New Roman""><o:p></o:p></span></b></p><p style="border:none;mso-border-bottom-alt:double windowtext 2.25pt;
padding:0cm;mso-padding-alt:0cm 0cm 1.0pt 0cm"><b><span lang="EN-GB" style="font-size:12.0pt;mso-bidi-font-size:10.0pt;
font-family:"Times New Roman";mso-ansi-language:EN-GB">Contact</span></b><span lang="EN-GB" style="font-size:12.0pt;mso-bidi-font-size:10.0pt;font-family:"Times New Roman";
mso-ansi-language:EN-GB">: Pierre-Yves Oudeyer, <a href="mailto:pierre-yves.oudeyer@inria.fr">pierre-yves.oudeyer@inria.fr</a> <br>
<b>Deadline for applying</b>: 19<sup>th</sup>
april 2018<br>
<b>How to apply</b>: send email to PY
Oudeyer and post application to <a href="https://jobs.inria.fr/public/classic/fr/offres/2018-00413">https://jobs.inria.fr/public/classic/fr/offres/2018-00413</a>
<br>
<!--[if !supportLineBreakNewLine]--><span style="font-family: Helvetica; font-size: 14px;"><span lang="EN-US" style="font-size: 12pt; line-height: 18px; font-family: 'Times New Roman';"><b>Duration: </b>16 months</span></span>
<!--[endif]--><o:p></o:p></span></p>
</div><p style="text-align:justify"><b><span lang="EN-US" style="font-size:12.0pt;mso-bidi-font-size:10.0pt;font-family:"Times New Roman"">Scientific
context</span></b><span lang="EN-US" style="font-size:12.0pt;mso-bidi-font-size:
10.0pt;font-family:"Times New Roman"">: <o:p></o:p></span></p><p class="MsoNormal" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:
justify"><span lang="EN-GB" style="font-size:12.0pt;mso-bidi-font-size:11.0pt;
line-height:115%;font-family:"Times New Roman";mso-ansi-language:EN-GB">Intrinsically
motivated goal exploration algorithms enable machines to discover repertoires
of action policies that produce a diversity of effects in complex environments.
In robotics, these exploration algorithms have been shown to allow real world
robots to acquire skills such as tool use in high-dimensional continuous state
and action spaces (e.g. Baranes and Oudeyer, 2013; Forestier et al., 2017). An
example is the Poppy/Explauto experiment with a humanoid robot learning to use
tools in a few hours, which obtained the 2<sup>nd</sup> position in the
demonstration competition at NIPS 2016 (See
<a href="https://www.youtube.com/watch?v=NOLAwD4ZTW0">https://www.youtube.com/watch?v=NOLAwD4ZTW0</a>
, Forestier et al., 2016). In other domains such as chemistry and physics, they
open the possibility to automate the discovery of novel chemical or physical
structures produced by complex dynamical systems (e.g. Grizou, Points et al.,
2017) <o:p></o:p></span></p><p class="MsoNormal" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:
justify"><span lang="EN-GB" style="font-size:12.0pt;mso-bidi-font-size:11.0pt;
line-height:115%;font-family:"Times New Roman";mso-ansi-language:EN-GB"> </span></p><p class="MsoNormal" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:
justify"><span lang="EN-GB" style="font-size:12.0pt;mso-bidi-font-size:11.0pt;
line-height:115%;font-family:"Times New Roman";mso-ansi-language:EN-GB">However,
they have so far assumed that self-generated goals are sampled in a
specifically engineered feature space, limiting their autonomy. Recent work has
shown how unsupervised deep learning approaches could be used to learn goal
space representations (e.g. Péré et al., 2018), but they have focused on goals
represented as static target configurations of the environment in robotics
sensorimotor spaces. <o:p></o:p></span></p><p class="MsoNormal" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:
justify"><span lang="EN-GB" style="font-size:12.0pt;mso-bidi-font-size:11.0pt;
line-height:115%;font-family:"Times New Roman";mso-ansi-language:EN-GB"> </span></p><p class="MsoNormal" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:
justify"><span lang="EN-GB" style="font-size:12.0pt;mso-bidi-font-size:11.0pt;
line-height:115%;font-family:"Times New Roman";mso-ansi-language:EN-GB">In this
project, we will study how these new families of machine learning algorithms
can be extended and used for automated discovery of behaviours of dynamical
systems in physics/chemistry, through exploration of their parameter space, and
when these behaviours are perceived through raw sensors (e.g. pixels of
images).<o:p></o:p></span></p><p style="text-align:justify"><span lang="EN-GB" style="font-size:12.0pt;
font-family:"Times New Roman";mso-ansi-language:EN-GB">Web site of Flowers Lab:
<a href="https://flowers.inria.fr">https://flowers.inria.fr</a><o:p></o:p></span></p><p style="text-align:justify"><b><span style="font-size:12.0pt;mso-bidi-font-size:10.0pt;font-family:"Times New Roman";
mso-ansi-language:FR">Post-doctoral researcher work description</span></b><b><span lang="EN-GB" style="font-size:12.0pt;
mso-bidi-font-size:10.0pt;font-family:"Times New Roman";mso-ansi-language:EN-GB"> <o:p></o:p></span></b></p><p style="text-align:justify"><span lang="EN-US" style="font-size:12.0pt;
mso-bidi-font-size:10.0pt;font-family:"Times New Roman"">The work will begin by
a familiarization with algorithms for intrinsically motivated exploration with
unsupervised learning of goal spaces developed in the team. Then, a few
interesting numerical/simulation models of physical/chemical phenomena,
associated to rich behavioral patterns (such as Turing-like reaction-diffusion
models or oil droplet models), will be selected as a basis for developing and
experimenting updates of existing exploration and learning algorithms. These
algorithmic updates will focus in particular on the use of recurrent neural
networks to learn goal spaces that can encode dynamical phenomena. Several
algorithmic implementations will be compared, e.g. using several techniques for
encoding times series as goal spaces, and compared using various information
theoretic measures of the diversity of discovered behaviours in the target
dynamical systems. It will be also possible to test these algorithmic updates
in more traditional (simulated) sensorimotor learning problems, using standard
benchmarks of the literature (e.g. Open Gym environments or Minecraft/Malmo). <o:p></o:p></span></p><p style="text-align:justify"><span lang="EN-GB" style="font-size:12.0pt;
mso-bidi-font-size:10.0pt;font-family:"Times New Roman";mso-ansi-language:EN-GB">Experiments
will possibly extend the DeepExplauto open-source library developed in the team,
as well as standard Deep Learning libraries for the algorithms. <o:p></o:p></span></p><p class="MsoNormal" style="text-align:justify"><b><span lang="EN-GB" style="font-size:12.0pt;line-height:115%;font-family:
"Times New Roman";mso-fareast-font-family:"ＭＳ 明朝";mso-ansi-language:EN-GB;
mso-fareast-language:FR">Required knowledge and background:<o:p></o:p></span></b></p><p class="MsoNormal"><span lang="EN-US" style="font-size:12.0pt;mso-bidi-font-size:
10.0pt;line-height:115%;font-family:"Times New Roman";mso-fareast-font-family:
"ＭＳ 明朝";mso-ansi-language:EN-US;mso-fareast-language:FR">Candidates should have
a strong expertise in at least one of these areas:<br>
- Experience with Deep Learning algorithms (theory and practical
implementations)<br>
- Stochastic optimization, black-box optimization<br>
- Intrinsically motivated exploration algorithms <o:p></o:p></span></p><p class="MsoNormal"><span lang="EN-US" style="font-size:12.0pt;mso-bidi-font-size:
10.0pt;line-height:115%;font-family:"Times New Roman";mso-fareast-font-family:
"ＭＳ 明朝";mso-ansi-language:EN-US;mso-fareast-language:FR">Knowledge and
competence in physics/chemical modelling would be welcome. <o:p></o:p></span></p><p class="MsoNormal"><span lang="EN-US" style="font-size:12.0pt;mso-bidi-font-size:
10.0pt;line-height:115%;font-family:"Times New Roman";mso-fareast-font-family:
"ＭＳ 明朝";mso-ansi-language:EN-US;mso-fareast-language:FR">Other requirements:<br>
- strong skills in mathematics, statistical inference, machine learning<br>
- Advanced programming skills in script languages like python <br>
- Motivation to work in an interdisciplinary project</span></p><p class="MsoNormal"><b><span lang="EN-GB" style="font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:115%;font-family:
"Times New Roman";mso-ansi-language:EN-GB">References :<o:p></o:p></span></b></p><p><span lang="EN-GB" style="font-size:12.0pt;font-family:"Times New Roman";
mso-ansi-language:EN-GB">Baranes, A., & Oudeyer, P. Y. (2013). Active
learning of inverse models with intrinsically motivated goal exploration in
robots. Robotics and Autonomous Systems, 61(1), 49-73.<o:p></o:p></span></p><p><span lang="EN-GB" style="font-size:12.0pt;font-family:"Times New Roman";
mso-ansi-language:EN-GB">Forestier, S., Mollard, Y., Caselli, D., &
Oudeyer, P. Y. (2016). Autonomous exploration, active learning and human
guidance with open-source Poppy humanoid robot platform and Explauto library.
In The Thirtieth Annual Conference on Neural Information Processing Systems
(NIPS 2016).<o:p></o:p></span></p><p><span lang="EN-GB" style="font-size:12.0pt;font-family:"Times New Roman";
mso-ansi-language:EN-GB">Grizou, J., Point, L. and the Cronin lab (2017)
Tutorial on “</span><span style="font-size:12.0pt;font-family:"Times New Roman";
mso-ansi-language:FR;mso-bidi-font-weight:bold">Developmental robotics in a
chemistry lab », <a href="https://croningp.github.io/tutorial_icdl_epirob_2017/">https://croningp.github.io/tutorial_icdl_epirob_2017/</a>.
<o:p></o:p></span></p><p><span style="font-size:12.0pt;font-family:"Times New Roman";mso-ansi-language:
FR;mso-bidi-font-weight:bold">Péré, A., Forestier, S., Oudeyer, P-Y. (2018)
Unsupervised learning of goal spaces for intrinsically motivated goal
exploration, in proceedings of ICLR 2018. <o:p></o:p></span></p><p><span lang="EN-GB" style="font-size:12.0pt;font-family:"Times New Roman";
mso-ansi-language:EN-GB">Points, L. J., Taylor, J. W., Grizou, J., Donkers, K.,
& Cronin, L. (2018). Artificial intelligence exploration of unstable
protocells leads to predictable properties and discovery of collective
behavior. Proceedings of the National Academy of Sciences, 201711089.<o:p></o:p></span></p><p><span style="font-size:12.0pt;font-family:"Times New Roman";mso-ansi-language:
FR">LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. <i>Nature</i>,
<i>521</i>(7553), 436-444.<o:p></o:p></span></p><p class="MsoNormal"><b><span lang="EN-US" style="font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:115%;font-family:
"Times New Roman";mso-ansi-language:EN-US">Keywords</span></b><span lang="EN-US" style="font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:115%;font-family:
"Times New Roman";mso-ansi-language:EN-US">: deep learning; intrinsically
motivated exploration; unsupervised learning; automated discovery; complex
systems; dynamical systems</span></p><p class="MsoNormal">Pierre-Yves Oudeyer</p><div>Research director, Flowers lab</div><div>Inria and Ensta ParisTech, France</div><div><a href="http://www.pyoudeyer.com">http://www.pyoudeyer.com</a></div><div><br></div><p class="MsoNormal" style="margin-bottom: 0.0001pt;"><span lang="EN-US" style="font-size: 12pt; font-family: 'Times New Roman';"> </span></p>
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