Up to 3 postdoctoral positions are available at the center for sleep and consciousness science in the laboratory of Dr. Giulio Tononi (University of Wisconsin, Madison), to study the relation of information and causation within the framework of the integrated information theory of consciousness (Balduzzi & Tononi, 2008; Tononi, 2012).
Immediate funding is available for a range of projects related to foundational questions regarding the ontological status of information, and its relation to causation, emergence, adaptation, evolution, and consciousness (see detailed scope of work below).
Successful candidates will work at the center for sleep and consciousness science, dedicated to a broad range of research problems focused on two neurobiological problems – the mechanisms and functions of sleep and the neural substrates of consciousness.
Candidates are expected to have strong training in an analytically rigorous discipline such as theoretical biology/neuroscience, physics, mathematics, computer science, or engineering. Experience in information theory, complex systems, and an interest in the philosophy of information/causation are a plus. Programming experience is required (knowledge of MATLAB and/or C++ is of advantage).
Appointments are renewable from year to year for up to 3 years, starting as soon as possible or until the positions are filled. Post-doc salaries correspond to the National Institutes of Health National Research Service Award (NRSA) stipend schedule for postdoc trainees, based on number of years of postdoctoral experience.
Candidates should send a CV, brief statement of previous research and future research interests, and email addresses and phone numbers of three references to: Giulio Tononi, firstname.lastname@example.org.
Balduzzi D, Tononi G (2008) Integrated information in discrete dynamical systems: motivation and theoretical framework. PLoS Comput Biol 4:e1000091.
Tononi G (2012) Integrated Information Theory of Consciousness: An Updated Account. Arch Ital Biol 150:56–90.
The scope of work will range from:
- theoretical development and computational implementation of the notion of intrinsic causal information
- causal analysis, including the systematic use of perturbations, counterfactuals, and irreducibility
- analysis of complex systems in terms their causal/informational structure
- meaning (understanding and control) from the intrinsic perspective of the system
- actual causation (causal explanation)
- examining similarities and differences between existing notions of information and causation (Shannon information, algorithmic information,…)
- defining emergence in terms of the spatio-temporal grain size at which a system of elements achieves a maximum of causal power
- evolutionary/adaptive aspects of information/causation including simulations based on small adaptive neural networks (animats), which evolve and adapt to an environment that requires sensitivity to context for survival