looking for outstanding candidates for a PhD studentship and a Postdoc
position on the Theory of Evolutionary Computation and other
Bio-Inspired Algorithms, with close links to Population Genetics.
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PHD STUDENTSHIP ON THEORY OF BIO-INSPIRED COMPUTATION
The Department of Computer Science at the University of Sheffield is
offering a fully funded 3 year PhD studentship on theoretical
foundations of Bio-Inspired Computation.
Bio-inspired algorithms include general-purpose metaheuristics like
evolutionary algorithms, ant colony optimisation and particle swarm
optimisation. The idea is to mimic powerful mechanisms from nature, such
as the natural evolution of species or the collective intelligence of a
swarm of animals, and to apply these mechanisms for solving complex
optimisation problems. Bio-inspired algorithms have been applied to a
broad range of problems in various disciplines with remarkable success.
They are particularly useful in settings where no knowledge on the
problem is available (black-box optimisation) and evaluating candidate
solutions is the only means of learning about the problem in hand.
However, the reasons behind their success are often elusive: their
performance often depends crucially, and unpredictably, on design
choices and parameters. This lack of understanding represents a major
obstacle for the uptake and usage of bio-inspired algorithms and for
developing more effective variants thereof.
In recent years theoretical analyses have emerged that provide such an
understanding through studying the performance of bio-inspired
algorithms. They rigorously estimate the expected time until such an
algorithm finds a satisfactory solution for various optimisation
problems; a vital stepping stone towards designing more efficient
bio-inspired algorithms. Analyses use mathematical techniques from the
analysis of randomised algorithms, probability theory and computational
complexity. The results allow for insights into the working principles
of bio-inspired metaheuristics, enable the assessment of parameter
choices and design aspects, and contribute to the design of more
powerful algorithms.
This studentship offers a valuable opportunity to work within this very
active, challenging, and exciting field at the intersection of
computational complexity and bio-inspired computation. In addition,
candidates may contribute to the upcoming SAGE project (Speed of
Adaptation in Population Genetics and Evolutionary Computation,
http://www.project-sage.eu), an ambitious interdisciplinary project
funded by the EU's Future and Emerging Technologies scheme. SAGE aims at
bringing together Population Genetics and Evolutionary Computation to
develop a unified theory describing the speed of adaptation in both
biological and artificial evolution.
Applicants should have, or expect to achieve, a minimum of an
upper-second-class Honours degree (2.1 or above) or a Master´s degree in
Computer Science, Mathematics, or related disciplines (or equivalent).
The project is mathematically challenging. Expertise with computational
complexity, probability theory and bio-inspired computation is
desirable. Good analytical thinking and an interest in these areas are
essential.
The award covers UK/EU tuition fees and a stipend at the standard UK
research rate of £13,726 per annum.
UK applicants and EU applicants are eligible for a full scholarship
award. International non-EU applicants cannot be funded and are not
eligible to apply.
Applicants need to apply before December 15, 2013 using the online
application form at:
http://www.shef.ac.uk/postgraduate/research/apply
Please send informal enquiries to Dirk Sudholt, d.sudholt@sheffield.ac.uk
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RESEARCH ASSOCIATE IN EVOLUTIONARY COMPUTATION AND POPULATION GENETICS
A Research Associate position is available to work on a unified
quantitative theory of evolution that describes the efficiency of
evolution in both natural systems and evolutionary algorithms. The
position is part of SAGE (Speed of Adaptation in Population Genetics and
Evolutionary Algorithms, see http://www.project-sage.eu/ ), a new
European project in the Future and Emerging Technologies scheme. It is
based in the Computational Biology Group in the Department of Computer
Science, and reports to Dr Dirk Sudholt.
SAGE is a joint effort between 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 unified quantitative theory that describes how quickly
populations adapt to evolve high-fitness individuals in both natural and
artificial evolution. Our goal is to reveal how this efficiency is
fundamentally determined by evolutionary and environmental parameters,
and how these parameters can be tuned to use evolution most effectively
in applications ranging from evolutionary algorithms to experimental
evolution and synthetic biology. The post involves close collaboration
with colleagues from different disciplines, in particular computer
scientists in Sheffield, Nottingham, and Jena, and theoretical
population biologists in the Barton group at IST Austria.
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 post offers a salary in the range of £28,685 - £36,298 per annum. It
is available from January 2014, for a period of 36 months.
Further details and an online application form are available from
http://www.dcs.shef.ac.uk/~dirk/sage-ad.html
Applicants need to apply through the above online system before November
28, 2013.
Please send informal enquiries to Dirk Sudholt, d.sudholt@sheffield.ac.uk
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