Speed of Adaptation in Population Genetics and Evolutionary Computation

SAGE is a 2M euro EU-project which brings together an interdisciplinary consortium of researchers from the theory of evolutionary  computation and theoretical population genetics to develop a unified theory describing the speed of adaptation in both biological and artificial evolution.  The impact of this unified theory will lie in enabling long-term predictions about the efficiency of evolution in settings that are highly relevant for both fields and related sciences.

Project Description

Biological evolution has produced an extraordinary diversity of organisms, even the simplest of which is highly adapted, with multiple complex structures. Evolutionary computation has found that many innovative solutions to optimisation and design problems can be achieved by artificial evolution via random variation and selection.

Despite the centrality of evolution to biology and the usefulness of evolutionary algorithms in optimisation, the dynamics of evolution are not well understood. Consequently, population genetics theory can only make quantitative predictions about short-term, simple biological evolution, and the design and parameter tuning of evolutionary algorithms is mostly done ad-hoc in a laborious and cost-intensive process.

Both fields have studied the speed of adaptation independently, and with orthogonal approaches. Our project brings together an interdisciplinary consortium of ambitious researchers from the theory of evolutionary computation and theoretical population genetics to synergise these complementary approaches and to create the foundation of a unified quantitative theory describing the speed of adaptation in both biological and artificial evolution.

The transformative impact of this unified theory will lie in enabling long-term predictions about the efficiency of evolution in settings that are highly relevant for both fields and related sciences. Our approach will reveal how this efficiency is fundamentally determined by evolutionary and environmental parameters. Tuning these parameters will allow researchers from biology and computation to increase the efficiency of evolutionary processes, revolutionising applications ranging from evolutionary algorithms to experimental evolution and synthetic biology.

Open Positions

Four outstanding postdoctoral candidates are required to support the project. This project brings together four European research institutions - University of Nottingham, University of Sheffield, Friedrich Schiller University Jena, and IST Austria - to develop world-leading research at the interface between Population Genetics and Computer Science. Specifically, SAGE aims at bringing together these two research fields to develop a new quantitative theory of the efficiency of evolutionary processes in natural and artificial evolution.

Applications are invited from highly skilled researchers in Computer Science, Mathematics, Physics, Theoretical Biology or related areas (at the interface between computer science and biology). A good understanding of evolutionary computation and/or population genetics will be an advantage. In addition, strong mathematical and analytical skills are essential.

The applicants must have (or be very close to completing) a PhD in Computer Science, Biology, Mathematics, Physics or related disciplines. The successful candidates will be working on the development of a generalised theory of evolution covering both artificial evolution, such as in evolutionary algorithms, as well as natural evolution, and integrating methods and tools from both fields, including runtime analysis and the diffusion approximation, amongst others. They should be able to work independently as well as in multidisciplinary teams. Good communication and presentation skills are crucial. The candidates are expected to disseminate research results in peer-reviewed journals and conferences.

The post offers a competitive salary (TV-L E13, range 42,000€ - 60,500€ p.a. depending on experience) and are available from January 2014, for a period of up to 36 months. Applications should include a CV, names of references and a letter of motivation. Applications for the position in Jena may be submitted until November 23 via email to Tobias Friedrich.  Further details are available on the project home page and the official vacancy notice 132/2013 (in German).