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Understanding the underlying algorithmic principles of biological systems, via the application of modelling techniques developed in artificial intelligence to problems in ecology and evolution. Example topics: the major transitions in evolution, the evolution of sex, symbiosis and symbiogenesis, ecosystem selection in biofilms, collective building behaviour in insects, the evolution of signalling and communication, social learning behaviour, mimicry, the epistemological status of evolutionary simulation models.

Critical drivers in the life sciences require that we better understand the interconnected organisation and adaptation of ecological and evolutionary systems. Key applications include bio-engineering and synthetic biology (e.g., bio-transformation in chemical industries, bio-regeneration and sustainability in agriculture, and epidemiology and disease treatments in medicine). Moreover, understanding how biological systems cope with and exploit complexity will also inform bio-inspired engineering of artificial complex adaptive systems (e.g., robust and adaptable approaches to distributed sensor networks, amorphous computation, collective robotics, or indeed, high-performance parallel computing).