Postdoctoral Position : Agent-Based Modeling of Social Complexity in Ancient Egypt : University of Cape Town, South Africa

The postdoctoral fellow will work on an interdisciplinary agent-based modeling (ABM) and simulation project that investigates the emergence of social complexity in early Egypt. The project proposes to develop the ABM as an experimental computational platform for studying and analyzing complex system behaviour, in this case, the evolution of societal complexity. The ABM will be used to design experiments that examine the social dynamics of early Egypt, including the emergence of entrenched inequality, urbanism, social hierarchy, networks, and ideology of kingship. The goal is to explore how the Egyptian state emerged as a result of the meaningful actions of individuals pursuing their own interests within the particular environmental conditions of the Nile Valley in the fourth millennium BC, as well as compare this system to similar case studies in social complexity in Africa more broadly.

As part of the process of developing the ABM, the fellow will be expected to conduct research on the modeling of emergent complexity in agent-based models of ancient societies, including the application of evolutionary machine learning to simulate adaptive behaviour. Ideally the ABM design principles will take inspiration from the relevant social complexity literature and prevailing theories of emergent complexity.  However, the exact focus of the project will be jointly decided by the postdoctoral fellow and supervisors.

The candidate will have the opportunity to collaborate with the interdisciplinary network of researchers at the Evolutionary Machine Learning Group, University of Cape Town, the Department of Ancient Studies, Stellenbosch University, and the Department of Archaeology, University of Cape Town. In addition to research, candidate is expected to co-supervise graduate students within this network of researchers.

Requirements:

* PhD (or nearly completed) degree in computational archaeology, computer science, or a closely related field.

* Good programming skills (Java, Python, Net Logo or other agent-based modeling languages).

* Excellent communication skills, in both spoken and written English, and the ability to work independently.

* Expertise in agent-based modeling and simulation.

* Some expertise in evolutionary machine learning would be advantageous.

* Candidates with a background in computational archaeology who are willing to acquire machine learning expertise during the postdoc, are encouraged to apply.

Deadlines and More Information:

Starting date is flexible: From February 1, 2020.

Applications will be evaluated on a first-come-first-serve basis, and will continue to be received and reviewed from December 1, 2019 until the position is filled.

Contact for more information: Geoff Nitschke (gnitschke@cs.uct.ac.za).

Geoff Nitschke

Director – Evolutionary Machine Learning Group https://people.cs.uct.ac.za/~gnitschke/research.html

Department of Computer Science, University of Cape Town

South Africa