Postdoctoral / Research Fellow position in complex network analysis: Critical events detection Postdoctoral or Research Fellow position is available to join the Complex Networks and Brain Dynamics group for the project: “Modelling and analysis of complex systems for safety of critical infrastructures“ as part of the National Center of Competence – Cybernetics and Artificial Intelligence funded by the Technology Agency of the Czech Republic, and related projects. The project involves developing, implementing, optimizing and applying techniques for detection and prediction of critical events and regime transitions and their propagation in complex networks, with applications in societally important real-world systems such as social and communication networks, computer networks and large-scale industrial systems. Conditions: • Initial contract is for 6 months duration (with possible extension up to 30 months based on project progress). • Positions are available immediately with starting date upon agreement. • Applications will be reviewed on a rolling basis with a first cut-off point on 30. 9. 2022. • This is a full-time fixed term contract appointment. Part time contract negotiable. • Monthly gross salary: 45 000 - 54 000 CZK based on qualifications and experience. Cost Of Living Comparison • Bonuses depending on performance and travel funding for conferences and research stays. • No teaching duties.

Postdoctoral / Junior Scientist position in Complex Networks and Information Theory A Postdoc or Junior Scientist position is available to join the Complex Networks and Brain Dynamics group for the project: “Network modelling of complex systems: from correlation graphs to information hypergraphs“ funded by the Czech Science Foundation. The project involves developing, optimizing and applying techniques for modelling complex dynamical systems beyond the currently available methods of complex network analysis and game theory. The project is carried out in collaboration with the Artificial Intelligence Center of the Czech Technical University. Conditions: • Contract is of 18 months duration (with the possibility of follow-up tenure-track application). • Starting date: position is available immediately. • Applications will be reviewed on a rolling basis with a first cut-off point on 30. 9. 2022. • This is a full-time fixed term contract appointment. Part time contract negotiable. • Monthly gross salary: 42 000 - 48 000 CZK based on qualifications and experience. Cost Of Living Comparison • Bonuses depending on performance and travel funding for conferences and research stays. • Contribution for reallocation costs for succesful applicant coming from abroad: 10 000 CZK plus 10 000 CZK for family (spouse and/or children). • No teaching duties

Research Fellow / Postdoc positions in Complex Networks and Brain Dynamics We are looking for new team members to join the Complex Networks and Brain Dynamics group to work on its interdisciplinary projects. The group is part of the Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences - based in Prague, Czech Republic, https://www.cs.cas.cz/. We focus on the development and application of methods of analysis and modelling of real-world complex networked systems, with particular interest in the structure and dynamics of human brain function. Our main research areas are neuroimaging data analysis (fMRI & EEG, iEEG, anatomical and diffusion MRI), brain dynamics modelling, causality and information flow inference, nonlinearity and nonstationarity, graph theory, machine learning and multivariate statistics; with applications in neuroscience, climate research, economics and general communication networks. More information about the group at http://cobra.cs.cas.cz/. Conditions: • Contract is for 6-24 months duration. • Positions are available immediately or upon agreement. • Applications will be reviewed on a rolling basis with a first cut-off point on 30. 09. 2022, until the positions are filled. • This is a full-time fixed term contract appointment. Part time contract negotiable. • Monthly gross salary: 42 000 – 55 000 CZK based on qualifications and experience. Cost Of Living Comparison • Bonuses and travel funding for conferences and research stays depending on performance. • No teaching duties.

The Santa Fe Institute seeks applications for postdoctoral fellows for the 2024 cohort. The fellowships offer early-career scholars the opportunity to undertake their own independent research within a collaborative research community that nurtures creative, transdisciplinary thought in pursuit of key insights about the complex systems that matter most for science and society. Postdoctoral Fellows spend up to three years in residence at SFI, where they contribute to SFI’s research in the sciences of complexity and are trained to become leaders in interdisciplinary science. The fellowships offer a competitive salary, generous benefits, and discretionary research funding. The city of Santa Fe offers a remarkable quality of life and year-round opportunities for outdoor activities. Interdisciplinary scholars with broad interests with interests in any scientific discipline, including AI, machine learning, and cognitive science, are encouraged to apply.

The Bertalanffy Doctoral Student Award is part of CSS FRANCE's global initiative to support early career researchers in their quest to advance the frontiers of science across a broad range of disciplines. It is in place to recognize early career contributions and leadership in research in Complex Systems related fields. It is awarded to young researchers enrolled in a Ph.D. program. This competition consists in presenting your research in simple terms in a five minutes video to a lay audience. Your presentation should be clear, concise, and convincing.

The Young Researcher Award is part of CSS FRANCE's global initiative to support early career researchers in their quest to advance the frontiers of science across a broad range of disciplines. It is in place to recognize early career contributions and leadership in research in Complex Systems related fields. It is awarded to young researchers up to five years after the Ph.D. completion (date of Ph.D. defense) and the deadline of the call for nomination.

The RADLAB at Northeastern University’s Network Science Institute has two postdoctoral positions available. We are looking for exceptional candidates who are interested in the following programs: 1. Trustworthy Network Science: As the use of machine learning in network science grows, so do the issues of stability, robustness, explainability, transparency, and fairness, to name a few. We address issues of trustworthy ML in network science. 2. Just Machine Learning: Machine learning systems are not islands. They are part of broader complex systems. To understand and mitigate the risks and harms of using machine learning, we remove our optimization blinders and study the broader complex systems in which machine learning systems operate.

Measuring the degree of consciousness an organism possesses has remained a longstanding challenge in Neuroscience. In part, this is due to the difficulty of finding the appropriate mathematical tools for describing such a subjective phenomenon. Current methods relate the level of consciousness to the complexity of neural activity, i.e., using the information contained in a stream of recorded signals they can tell whether the subject might be awake, asleep, or anaesthetised. Usually, the signals stemming from a complex system are correlated in time; the behaviour of the future depends on the patterns in the neural activity of the past. However these past-future relationships remain either hidden to, or not taken into account in the current measures of consciousness. These past-future correlations are likely to contain more information and thus can reveal a richer understanding about the behaviour of complex systems like a brain. Our work employs the "epsilon-machines” framework to account for the time correlations in neural recordings. In a nutshell, epsilon-machines reveal how much of the past neural activity is needed in order to accurately predict how the activity in the future will behave, and this is summarised in a single number called "statistical complexity". If a lot of past neural activity is required to predict the future behaviour, then can we say that the brain was more “awake" at the time of recording? Furthermore, if we read the recordings in reverse, does the difference between forward and reverse-time statistical complexity allow us to quantify the level of time asymmetry in the brain? Neuroscience predicts that there should be a degree of time asymmetry in the brain. However, this has never been measured. To test this, we used neural recordings measured from the brains of fruit flies and inferred the epsilon-machines. We found that the nature of the past and future correlations of neural activity in the brain, drastically changes depending on whether the fly was awake or anaesthetised. Not only does our study find that wakeful and anaesthetised fly brains are distinguished by how statistically complex they are, but that the amount of correlations in wakeful fly brains was much more sensitive to whether the neural recordings were read forward vs. backwards in time, compared to anaesthetised brains. In other words, wakeful fly brains were more complex, and time asymmetric than anaesthetised ones.

Far-from-equilibrium processes constantly dissipate energy while converting a free-energy source to another form of energy. Living systems, for example, rely on an orchestra of molecular motors that consume chemical fuel to produce mechanical work. In this talk, I will describe two features of life, namely, time-irreversibility, and nonequilibrium self-assembly. Time irreversibility is the hallmark of nonequilibrium dissipative processes. Detecting dissipation is essential for our basic understanding of the underlying physical mechanism, however, it remains a challenge in the absence of observable directed motion, flows, or fluxes. Additional difficulty arises in complex systems where many internal degrees of freedom are inaccessible to an external observer. I will introduce a novel approach to detect time irreversibility and estimate the entropy production from time-series measurements, even in the absence of observable currents. This method can be implemented in scenarios where only partial information is available and thus provides a new tool for studying nonequilibrium phenomena. Further, I will explore the added benefits achieved by nonequilibrium driving for self-assembly, identify distinctive collective phenomena that emerge in a nonequilibrium self-assembly setting, and demonstrate the interplay between the assembly speed, kinetic stability, and relative population of dynamical attractors.

A rich data representation that finds wide application in industry and research is the so-called knowledge graph - a graph-based structure where entities are depicted as nodes and relations between them as edges. Complex systems like molecules, social networks and industrial factory systems can be described using the common language of knowledge graphs, allowing the usage of graph embedding algorithms to make context-aware predictions in these information-packed environments.

Natural systems provide both puzzles to unravel and demonstrations of what's possible. The natural world is full of complex systems of dynamically interchangeable, individually unreliable components that produce effective and reliable outcomes at the group level. A complementary goal to understanding the operation of such systems is that of being able to engineer artifacts that work in a similar way. One notable type of collective behavior is collective construction, epitomized by mound-building termites, which build towering, intricate mounds through the joint activity of millions of independent and limited insects. The artificial counterpart would be swarms of robots designed to build human-relevant structures. I will discuss work on both aspects of the problem, including studies of cues that individual termite workers use to help direct their actions and coordinate colony activity, and development of robot systems that build user-specified structures despite limited information and unpredictable variability in the process. These examples illustrate principles used by the insects and show how they can be applied in systems we create.

Networks are widely used as mathematical models of complex systems across many scientific disciplines, and in particular within neuroscience. In this talk, we introduce two aspects of our collaborative research: (1) machine learning and networks, and (2) graph dimensionality. Machine learning and networks. Decades of work have produced a vast corpus of research characterising the topological, combinatorial, statistical and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and sometimes overlapping) characteristics of a network. We have developed hcga, a framework for highly comparative analysis of graph data sets that computes several thousands of graph features from any given network. Taking inspiration from hctsa, hcga offers a suite of statistical learning and data analysis tools for automated identification and selection of important and interpretable features underpinning the characterisation of graph data sets. We show that hcga outperforms other methodologies (including deep learning) on supervised classification tasks on benchmark data sets whilst retaining the interpretability of network features, which we exemplify on a dataset of neuronal morphologies images. Graph dimensionality. Dimension is a fundamental property of objects and the space in which they are embedded. Yet ideal notions of dimension, as in Euclidean spaces, do not always translate to physical spaces, which can be constrained by boundaries and distorted by inhomogeneities, or to intrinsically discrete systems such as networks. Deviating from approaches based on fractals, here, we present a new framework to define intrinsic notions of dimension on networks, the relative, local and global dimension. We showcase our method on various physical systems.

Tom Froese is Assistant Professor at the Okinawa Institute of Science and Technology Graduate University (OIST), where he heads the Embodied Cognitive Science Unit. He is a cognitive scientist with a background in phenomenological philosophy, human-computer interaction, and complex systems theory. His interdisciplinary research centers on the role of agent-environment interaction in shaping cognition and consciousness, specifically when the interaction process involves sociality and technology. In this talk he will present current work in progress on “irruption theory”, a new theory of consciousness that integrates an embodied-enactive account of basic mind with radical formulations of the freedom and efficacy of intentional agency.

Physics offers countless examples for which theoretical predictions are astonishingly powerful. But it’s hard to imagine a similar precision in complex systems where the number and interdependencies between components simply prohibits a first-principles approach, look no further than the challenge of the billions of neurons and trillions of connections within our own brains. In such settings how do we even identify the important theoretical questions? We describe a systems-scale perspective in which we integrate information theory, dynamical systems and statistical physics to extract understanding directly from measurements. We demonstrate our approach with a reconstructed state space of the behavior of the nematode C. elegans, revealing a chaotic attractor with symmetric Lyapunov spectrum and a novel perspective of motor control. We then outline a maximally predictive coarse-graining in which nonlinear dynamics are subsumed into a linear, ensemble evolution to obtain a simple yet accurate model on multiple scales. With this coarse-graining we identify long timescales and collective states in the Langevin dynamics of a double-well potential, the Lorenz system and in worm behavior. We suggest that such an ``inverse’’ approach offers an emergent, quantitative framework in which to seek rather than impose effective organizing principles of complex systems.

The presentation will present the work of the Space Enabled Research Group at the MIT Media Lab. The mission of the Space Enabled Research Group is to advance justice in Earth’s complex systems using designs enabled by space. Our message is that six types of space technology are supporting societal needs, as defined by the United Nations Sustainable Development Goals. These six technologies include satellite earth observation, satellite communication, satellite positioning, microgravity research, technology transfer, and the infrastructure related to space research and education. While much good work has been done, barriers remain that limit the application of space technology as a tool for sustainable development. The Space Enabled Research Group works to increase the opportunities to apply space technology in support of the Sustainable Development Goals and to support space sustainability. Our research applies six methods, including design thinking, art, social science, complex systems, satellite engineering and data science. We pursue our work by collaborating with development leaders who represent multilateral organizations, national and local governments, non-profits and entrepreneurial firms to identify opportunities to apply space technology in their work. We strive to enable a more just future in which every community can easily and affordably apply space technology. The work toward our mission covers three themes: 1) Research to apply existing space technology to support the United Nations Sustainable Development Goals; 2) Research to design space systems that are accessible and sustainable; and 3) Research to study the relationship between technology design and justice. The presentation will give examples of research projects within each of these themes.

Complex systems can be modeled at various levels of granularity, e.g., we can model a person at the cognitive level, on the neuronal level, or down to the biochemical level. When multiple models represent the same system at different scales, we would like to be able to reason about the causal effects of interventions on each level in such a way that the models remain consistent across levels. In the first part of this talk, I consider which conditions must be fulfilled for two structural equation models (SEMs) to stand in such a causally consistent relation. In the second part of the talk, I present recent work on learning causally consistent SEMs across multiple levels, distinguishing between bottom-up (micro- to macro-level) and top-down (macro- to micro-level) approaches.

We live in the age of the network: Internet social neural ecosystems. This has become one of the main metaphors for how we think about complex systems. This view also dominates the account of brain function. The role of neuronsdescribed by Cajal as the "butterflies of the soul" has become diminished to leaky integrate-and-fire point objects in many models of neural network computation. It is perhaps not surprising that networkexplanations of neural phenomena use neurons as elementary particles andascribe all their wonderful capabilities to their interactions in a network. In the network view the Connectome defines the brain and the butterflies have no role. In this talk I'd like to reclaim some key computations from the networkand return them to their rightful place at the cellular and subcellular level. I'll start with a provocative look at potential computational capacity ofdifferent kinds of brain computation: network vs. subcellular. I'll then consider different levels of pattern and sequence computationwith a glimpse of the efficiency of the subcellular solutions. Finally I propose that there is a suggestive mapping between entire nodesof deep networks to individual neurons. This in my view is how we can walk around with 1.3 litres and 20 watts of installed computational capacity still doing far more than giant AI server farms.