Un postdoc que puede ser de interés,

Saludos,

Alberto

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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 below:

many thanks,

Ard Louis

<<>> LONGER DESCRIPTION BELOW <<>>

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. 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 by Andreas Wagner. 

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
A. A. LouisStudies 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 shapesK. Dingle, F. Ghaddar, P. Sulc, and A. A. Louis, bioarxiv
[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. LouisNature Comm. 9, 761 (2018)
[6] Deep learning generalizes because the parameter-function map is biased towards simple functionsG. Valle Pérez, C. Q. Camargo, A. A. Louis ICLR (2019)
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Prof. A. A. Louis                    Rudolf Peierls Centre for Theoretical Physics
[log in para visualizar]  Oxford University
phone: +44 (0)1865 273994 Clarendon Laboratory, Parks Rd, 
fax:   +44 (0)1865 273947    Oxford, OX1 3PU, UK
http://www-thphys.physics.ox.ac.uk/user/ArdLouis/
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Dr. Alberto Pascual-García
ETH Zürich
Institute of Integrative Biology
Theoretical Biology, CHN H 76.1
Universitätstrasse 16 8006, Zürich
Schweiz
https://apascualgarcia.github.io/
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