Un postdoc que puede ser de interés,



*From: *"Ard A. Louis" <[log in para visualizar]>
*Subject: **Oxford Postdoc in Theoretical Biological Physics -- deadline
Wed 5 May, midday UK time*

Dear colleagues,

We have a new 3-year PDRA position open for a project using statistical
mechanics and algorithmic information theory to investigate evolutionary
patterns in biological development.

The deadline is midday (UK time) Wed 5 May, and the application link is here
I would appreciate it if you can  pass this on to anyone you think might be
suitable for this post.

Further details on the project can be found  at:
http://www-thphys.physics.ox.ac.uk/people/ArdLouis/evolution.shtml  or

many thanks,

Ard Louis


*PDRA on Arrival of the fittest in development: *

"Nothing in Biology Makes Sense Except in the Light of Evolution." wrote
the great naturalist Theodosius Dobzhansky
<http://en.wikipedia.org/wiki/Theodosius_Dobzhansky>. This is true, but to
really understand evolution, a stochastic optimization process in an
extremely high dimensional space[1], will require techniques from
statistical physics.

Darwinian evolution proceeds by two steps. First, random mutations generate
new heritable phenotypic variation. Second, the process of natural
selection ensures that phenotypes with higher fitness are more likely to
dominate in a population over time. Research in evolutionary theory has
mainly focussed on the second step, natural selection. Much less is known
about the first step, the arrival of variation. For a popular introduction
to this topic, see the charming book: Arrival of the Fittest
<https://oneworld-publications.com/arrival-of-the-fittest.html> by Andreas

Much of our work in this area uses theoretical tools of statistical
mechanics and algorithmic information theory, together with computer
simulations, to study genotype-phenotype maps, which give access to the
structured role of variation in evolution.   As an example, see this paper
on phenotypic bias [2], where we quantitatively predict the frequencies
with which RNA structures appear in nature, without taking natural
selection into account. Instead, a very strong bias in the arrival of
variation dominates over selective pressures, a non-ergodic effect we call
the arrival of the frequent [3].

We have recently shown that such strong bias in the arrival of variation
also explains structural patterns in protein quaternary structures and gene
regulatory networks [4].  The big open question for this postdoc is whether
such patterns can be observed beyond these molecular phenotypes, at the
larger scales of the evolution of development.   Arguments based on the
coding theory from algorithmic information theory [5] suggest that strong
bias may hold also for some aspects of development, but that first needs to
be established (or falsified) explicitly.

In this project, you will use a wide range of techniques to explore the
mapping from genotypes to phenotypes in models of development, and to study
the adaptive evolutionary dynamics on these landscapes.   There are close
analogies to the question of why overparameterized deep neural networks
generalize so well [6], and part of the project may include an exploration
of these commonalities.

This is a challenging interdisciplinary project.   Strong quantitative
skills and a proven track record of creative and successful independent
research are the most important qualities we are looking for.  Experience
with biological physics is a plus, but not a requirement.

Finally, for an overview of our work in this area see this recent talk:
[1] Contingency, convergence and hyper-astronomical numbers in biological
evolution <http://dx.doi.org/10.1016/j.shpsc.2015.12.014>
A. A. Louis, Studies in History and Philosophy of Biological and Biomedical
Sciences *58*, 107 (2016)
[2] Phenotype bias determines how RNA structures occupy the morphospace of
all possible shapes
<https://www.biorxiv.org/content/10.1101/2020.12.03.410605v1>, K. Dingle,
F. Ghaddar, P. Sulc, and A. A. Louis, bioarxiv
[3] The arrival of the frequent: how bias in genotype-phenotype maps can
steer populations to local optima
, S. Schaper and A. A. Louis, PLoS ONE 9(2): e86635 (2014)
[4] Symmetry and simplicity spontaneously emerge from the algorithmic
nature of evolution, I. G. Johnston, K. Dingle,  S. F. Greenbury, C. Q.
Camargo, J. P.K. Doye, S. E. Ahnert, and A. A. Louis
[5] Input–output maps are strongly biased towards simple outputs
K. Dingle, C. Q. Camargo and A. A. Louis, Nature Comm. *9*, 761 (2018)
[6] Deep learning generalizes because the parameter-function map is biased
towards simple functions <https://arxiv.org/abs/1805.08522>, G. Valle
Pérez, C. Q. Camargo, A. A. Louis ICLR (2019)
Prof. A. A. Louis                    Rudolf Peierls Centre for Theoretical
[log in para visualizar]  Oxford University
phone: +44 (0)1865 273994 Clarendon Laboratory, Parks Rd,
fax:   +44 (0)1865 273947    Oxford, OX1 3PU, UK


Dr. Alberto Pascual-García
ETH Zürich
Institute of Integrative Biology
Theoretical Biology, CHN H 76.1
Universitätstrasse 16 8006, Zürich