Two postdoctoral research associate positions are available in the Insect Robotics group, led by Professor Barbara Webb at the University of Edinburgh, to work on computational neuroscience and robot models of learning in larval Drosophila, as part of the European FET-Open project MINIMAL (Miniature Insect Model of Active Learning).
You must either already have a PhD in a relevant area or be nearing completion of your PhD studies, and must have a track record of related publications. Excellent skills in programming are essential. Candidates with expertise in large scale spiking neuron simulations, behavioural analysis methods, and/or neuroanatomical databases will be preferred. Ideally, candidates would also have knowledge of invertebrate brain and behaviour models, and/or familiarity with issues in learning theory from a computational perspective.
The posts are available from 1st January 2014, ending December 30th 2017. The closing date and interviews will be in early December. Initial enquiries should be made directly to Barbara Webb <B.Webb@ed.ac.uk>.
MINIMAL is a European research project funded by the EC Seventh Framework FET-Open. The MINIMAL consortium consists of the University of Edinburgh (co-ordinating site); Leibniz Institute for Neurobiology (Germany); Centre for Genomic Regulation (Spain); and Brainwave Discovery (UK).
The project takes biological inspiration for a vision of small low-power devices that are able to learn rapidly and autonomously about environmental contingencies, enabling prediction and adaptive anticipatory actions. Larval Drosophila have fewer than 10,000 neurons, yet express a variety of orientation and learning behaviours, including non-trivial anticipatory actions requiring context-dependent evaluation of their value. Current computational learning theory cannot fully account for or replicate these capacities. We aim to develop a new foundation for understanding natural learning by developing a complete multilevel model of learning in larvae.
Our aims are: (1) to analyse at a fine scale how ongoing larval behaviour is controlled and altered by associative conditioning, linked to agent-based models that ground learning capabilities in sensorimotor control; (2) to build one-to-one computational neural models that can be validated by exploiting the recent expansion of the Drosophila neurogenetic toolkit to gain unprecedented ability to characterise and manipulate neural circuits during unconstrained behaviour; (3) to derive from these models novel, generalisable algorithms and circuit architectures that can be used to enhance the learning and anticipatory capabilities of machines.
You will be expected to work with other members of the project and other sites in the development of computational models of larval behaviour, larval neural circuits and learning algorithms.
RA1 will take a leading role in the development of the neural circuit model. This will be a spiking neural model that copies one-to-one the neurons in the olfactory learning pathway of Drosophila larvae. It will be interfaced to a behavioural simulation, and to a robot model, and will be used to test hypotheses about the key mechanisms and locations of synaptic modification, and how the acquisition of associations interacts with behavioural control. Predictions from the model will be tested by our project partners using neurogenetic methods to measure and alter neural function in freely behaving animals.
RA2 will take a leading role in the derivation of abstracted algorithms for efficient minimalist learning based on the behavioural and neural models investigated in the project. They will also, as appropriate, be involved in the development of a comprehensive data base, the Virtual Larvae Brain, of neural anatomy, connectivity and physiology of the brain of larval Drosophila, and in development of advanced methods for tracking and analysing larval behaviour under diverse sensory and learning conditions.
Both RAs will be crucially involved in the scientific direction of the project, in dissemination of project results through publications and scientific meetings, in supervision of PhD students associated with the project, in day-to-day management and regular reporting of progress.
The posts are based in the School of Informatics, the largest, longest established and highest quality research grouping in Informatics in the UK. The University of Edinburgh provides a range of tailored training and career development programmes for postdoctoral research staff and you will be strongly encouraged to participate.
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
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