[alife] Research Associates in 'Optimal Collective Decision-Making in Social Insect Colonies' (Two Posts)

James Marshall james.marshall at bristol.ac.uk
Mon May 18 07:25:34 PDT 2009


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  Research Associates in 'Optimal Collective Decision-Making in Social  
Insect Colonies' (Two Posts)
   
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  Applications are invited for two postdoctoral research associate  
(PDRA) positions to study the collective decision-making of social  
insects, based in the Department of Computer Science and the School of  
Biological Sciences, University of Bristol, and under the supervision  
of Dr James Marshall and Professor Nigel R. Franks, and with the  
collaboration of Dr Anna Dornhaus (Arizona) and Professor Tom Seeley  
(Cornell).

  This BBSRC funded, 3-year, £500k project will build on ground- 
breaking work by this team in understanding how collective decision- 
making by rock ants and honeybees may be organised so that the  
resulting collective decisions are statistically optimal, representing  
the best possible compromise between the speed and accuracy of  
decision-making. The speed-accuracy trade-off in decision-making has  
been widely recognised in individual organisms, such as humans  
(Ratcliff, 1978) and bumblebees (Chittka et al., 2003) and in highly  
cohesive animal societies, particularly ants (Franks et al., 2003) and  
honeybees (Seeley & Visscher, 2004). Our recent work has been inspired  
by statistically optimal models of decision-making in primate neural  
circuits, and has shown how simple models of collective decision- 
making by social insect colonies can also be parameterised to  
implement statistically optimal decision-making (Marshall et al.,  
2009). This synthesis of concepts and techniques from n
euroscience and insect socio-biology is one of the first studies in  
the rapidly emerging field of ‘Colony-Level Cognition’ (Marshall &  
Franks, 2009).

  The current project will have experimental and theoretical strands:  
experiments with rock ants (Bristol) and honeybees (Arizona/Cornell)  
will validate existing theoretical predictions using state-of-the-art  
RFID tag technology, and help inform further modelling.

  Two PDRA positions are open, with starting salaries up to £30,594:

  1.	Theoretical biologist (Department of Computer Science, University  
of Bristol): the ideal candidate will have a background in a numerate  
discipline, such as mathematics, physics, or computer science, and  
experience of modelling complex biological systems. Experience of some  
or all of stochastic ordinary differential equations, probability and  
statistics would be a distinct advantage. The successful candidate  
will work to extend the applicability of current models to more  
complex and biologically realistic decision-scenarios, and develop  
entirely new models, as well as converting their theoretical  
predictions into testable hypotheses for the experimental biologist  
(post 2) to validate. Opportunities to be closely involved in  
experimental design will be available, as well as to undertake some  
experimental work with rock ants.

  2.	Experimental biologist (School of Biological Sciences, University  
of Bristol): the ideal candidate will have a background in biological  
sciences; direct experience of working with social insects, especially  
ants and bees, would be a distinct advantage. Experience of both lab- 
based and field experimental work is also desirable, as are  
programming and data analysis skills to deal with the large volumes of  
data RFID experiments generate. The successful candidate will  
primarily undertake experimental work in the Ant Lab (Bristol) with  
Temnothorax albipennis, and will also undertake 3 field seasons of 3  
months each to conduct emigration experiments with Apis mellifera  
(Arizona, with advice from Cornell). The experiments with ants and  
honeybees will utilise the latest RFID technology for tracking  
individual insects (e.g. Robinson et al. (2009)). Experimental design  
will be informed by the modelling predictions of the theoretical  
biologist (post 1), and opportunities to be invo
lved in design of theoretical models will also be available.

  References

  1.	Chittka, L., Dyer, A., Bock, F., Dornhaus, A. (2003) Bees trade  
off foraging speed for accuracy, Nature 424, 388
  2.	Franks, N. R., Dornhaus, A., Fitzsimmons, J. P. & Stevens, M.  
(2003) Speed versus accuracy in collective decision-making.  
Proceedings of the Royal Society B: Biological Sciences 270 (1532),  
2457 - 2463
  3.	Marshall, J. A. R., Bogacz, R., Planqué, R., Kovacs, T. & Franks,  
N. R. (2009) On optimal decision making in brains and social insect  
colonies. Journal of the Royal Society: Interface (doi: 10.1098/rsif. 
2008.0511)
  4.	Marshall, J. A. R. & Franks, N. R. (2009) Colony-level cognition.  
Current Biology 19, 10 (in press)
  5.	Ratcliff, R. (1978) A theory of memory retrieval.  Psychological  
Review 85, 59-108
  6.	Robinson, E. J. H., Smith, F. D., Sullivan, K. M. E. & Franks, N.  
R. (2009) Do ants make direct comparisons? Proceedings of the Royal  
Society Series B, (in press)
  7.	Seeley, T. D. & Visscher, P. K. (2004) Quorum-sensing during nest- 
site selection by honeybee swarms. Behavioral Ecology and Sociobiology  
56, 594-601

  Recent Media Coverage

  1.	‘Six Legs Goods’. Guardian g2 cover feature, 9th March 2009: http://www.guardian.co.uk/environment/2009/mar/09/ants-nature-research
  2.	‘Human Brain “Like Colony of Ants”’. Channel4.com, 25th February  
2009: http://www.channel4.com/news/articles/science_technology/human+brain+like+colony+of+ants/2995692
  3.	‘NIGEL FRANKS PROFILE: Watching as Ants Go Marching - and  
Deciding - One by One’. Science, 6th March 2009, vol. 323, pp 1284-1285.


  About the Department of Computer Science

  The Department of Computer Science is one of the top research-driven  
departments in the country. In the last HEFCE Research Assessment  
Exercise (2008) 70% of submitted academic staff were rated as  
‘internationally excellent’ or ‘world-leading’. The Intelligent  
Systems group, the largest research group within the Department,  
explores and exploits general principles underlying learning and  
intelligence in machines and biological systems. It focuses on Machine  
Learning: software that improves with experience; Computational  
Biology: models of biological systems; and Bio-inspired Computation:  
imitating nature's solutions. The Intelligent Systems group has just  
been awarded a total of £1.2m in grants from the Biotechnology and  
Biological Sciences Research Counil, of which this project is part.

  About the School of Biological Sciences

  The School of Biological Sciences is a dynamic, research-based  
School with a commitment to fundamental biology and cutting edge  
advances in new technologies. In the last HEFCE Research Assessment  
Exercise (2008) 50% of submitted academic staff were rated as  
‘internationally excellent’ or ‘world-leading’. Our research is  
distinctive in the range and depth of subject coverage. We have  
traditional strength in the biology of whole organisms and  
considerable expertise in the application of mathematical, molecular  
and biophysical methodologies to problems of behavioural,  
conservation, developmental, environmental and evolutionary biology.  
The Ant Lab has a 27 year history (18 at Bath and 9 at Bristol) of  
cutting edge insect research under the direction of Prof Nigel R.  
Franks, including 2 cover articles in Nature. Prof. Franks and the Lab  
were recently profiled in Science (Science (2009) Vol 323, 1284-2185).

Application Deadline

8:00 (GMT) 12th June 2009

Further Details
For informal enquiries, contact Dr James Marshall

For further details including how to apply, visit: http://www.bris.ac.uk/boris/jobs/ads?ID=80006


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James A. R. Marshall
Department of Computer Science
University of Bristol
www.cs.bris.ac.uk/~marshall







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