[alife] PhD'ship in evolutionary modelling, Southampton, UK.

[Richard A. Watson]

please distribute to your best ug/msc students looking for a phd - many thanks.

[http://sociam.org/sites/default/files/university_southampton_white_on_blue_0.png]PhD scholarship in Modelling the Extended Evolutionary Synthesis
Agents, Interactions & Complexity/ Institute for Life Sciences
Applications are invited for a PhD scholarship to start anytime between now and December 2016. This scholarship will fund students for their tuition fees, a stipend to cover living expenses and a Research Training Support Grant for conference attendance. Eligible for UK (fees and stipend) and EU (fees only) applicants (eligibility details<https://www.epsrc.ac.uk/skills/students/help/eligibility/>). This role will be supervised by Richard Watson in the Agents Interaction and Complexity Group within the Electronics and Computer Science Department, and affiliated with the Life Sciences Institute at Southampton.

[John_Templeton]<https://www.templeton.org/>The studentship is part of the worlds largest project to expand our understanding of evolution<https://isoton.wordpress.com/2016/04/11/worlds-largest-project-to-expand-understanding-of-evolution/> (1). This is a 7.7M pound project, with 22 inter-linked subprojects<http://synergy.st-andrews.ac.uk/ees/the-project>, led by Kevin Laland at St Andrews University and an international team of world-leading researchers (2) with funding from the John Templeton Foundation. This exciting project aims to put to the test the predictions of the extended evolutionary synthesis<http://synergy.st-andrews.ac.uk/ees/the-project> (3,4).
The two subprojects at Southampton, led by Dr. Richard Watson<http://www.ecs.soton.ac.uk/people/raw> (5), will build on recent developments unifying evolutionary theory with learning theory<http://www.sciencedirect.com/science/article/pii/S0169534715002931> (6). This work, recently featured on the cover of New Scientist<https://www.newscientist.com/issue/3066> (7), provides new theoretical tools to [https://d1o50x50snmhul.cloudfront.net/wp-content/uploads/2016/03/nsc_20160326-800x1052.jpg] <https://www.newscientist.com/article/mg22930660-100-evolution-learn-natural-selection/> develop our understanding of natural evolution. Formal equivalences between learning algorithms and evolutionary processes suggest that natural selection may be smarter than previously recognised - e.g. capable of learning from past experience to improve its capability to evolve (6). Both projects use computational modelling - this enables us to explore the adaptive capabilities of different functional processes and different assumptions about the selective conditions in which natural selection takes place, the variation on which it can act, and the heritability of that variation. Crucially, we can also model how these components of the Darwinian Machine (selection, variation and inheritance) change over time as a function of past evolution (see figure). Computational learning theory provides the conceptual and theoretical tools to formally characterise how evolution thereby changes itself over evolutionary time (6).
[Text Box:   Natural selection can modify the operation of variation, selection and inheritance via evo-devo, evo-eco and 'evo-ego' feedbacks (6)]One subproject focusses on the evolution of collective function and niche construction in ecosystems (8<https://biologydirect.biomedcentral.com/articles/10.1186/s13062-015-0094-1>) - co-investigators: John Odling-Smee (Oxford), Michael Wade (Indiana),Andrew Gardner (St Andrews). The other addresses the evolution of evolvability (9<http://onlinelibrary.wiley.com/doi/10.1111/evo.12337/abstract>,10<http://arxiv.org/abs/1508.06854>) - co-investigators: Gunter Wagner (Yale), Tobias Uller (Lund). As a part of this team, and also working closely with postdoctoral research fellows, the candidate may contribute to one or both subprojects.
The successful candidate will build mathematical/simulation models to develop our understanding of how these evo-eco or evo-eco feedbacks alter evolutionary dynamics and test the utility of learning theory to characterise them. Applicants must have a first degree (first class or upper-second class) in a relevant numerate subject (e.g. maths, computer science, evolutionary/theoretical biology), strong mathematical skills and experience in simulation modelling/programming. The ideal candidate will have an MEng/MSc (or equivalent, or near completion) with first class honours or distinction in their first degree. Knowledge of evolutionary theory and/or machine learning is desirable.
Prospective candidates are encouraged to contact Prof. Watson directly. To apply please send a CV, links to relevant publications, the names of two referees and a covering letter explaining your current interests and relevant background to raw1 at soton.ac.uk. Please note that the successful candidate will be asked to submit PhD research application online to ensure they have met all necessary admissions criteria.
http://www.southampton.ac.uk/courses/how-to-apply/postgraduate-applications.page
Further information about Prof. Watsons research:
http://www.ecs.soton.ac.uk/people/raw/research
(1) https://isoton.wordpress.com/2016/04/11/worlds-largest-project-to-expand-understanding-of-evolution/
(2) http://synergy.st-andrews.ac.uk/ees/the-project/
(3) http://synergy.st-andrews.ac.uk/ees/
(4) Laland, K.N. et al. (2015). The extended evolutionary synthesis: its structure, assumptions and predictions. Proc. R. Soc. B (Vol. 282, No. 1813, p. 20151019).
(5) http://www.ecs.soton.ac.uk/people/raw
(6) Watson, R.A., & Szathmary, E. (2016). How Can Evolution Learn? Trends in ecology & evolution, 31(2), 147-157.
(7) https://www.newscientist.com/issue/3066/
(8) Power, D.A., et al.. (2015). What can ecosystems learn? Expanding evolutionary ecology with learning theory. Biology direct, 10(1), 1-24.
(9) Watson, R.A. et al. (2014). The evolution of phenotypic correlations and 'developmental memory'. Evolution, 68(4), 1124-1138.;
(10) Kouvaris, K. et al. (2015). How Evolution Learns to Generalise: Principles of under-fitting, over-fitting and induction in the evolution of developmental organisation. Preprint arXiv:1508.06854.
We aim to be an equal opportunities employer and welcome applications from all sections of the community.
http://users.ecs.soton.ac.uk/raw/PhDadvertWatsonEES.htm