Complex Systems Studies

#PhD position in computational social science at TU Graz https://jobs.tugraz.at/en/jobs/54e87f3a-36f8-a13a-b6ae-6577fa57cbfd

92 Tweets from a mathematical physicist on Entropy in form of a book: http://math.ucr.edu/home/baez/what_is_entropy.pdf

#postdoc to develop an Agent-Based Model (#ABM) that simulates the socio-economic impacts of local transformation processes on the economy and population. ℹ️ csh.ac.at/engage/jobs/

From Ronald Ross to ChatGPT: the birth and strange life of the random walk Jordan Ellenberg https://youtu.be/08FGB5x090M?si=DFdtl3BrMvSddywL Between 1905 and 1910 the idea of the random walk, now a major topic in applied maths, was invented simultaneously and independently by multiple people in multiple countries for completely different purposes – in the UK, the story starts with Ronald Ross and the problem of mosquito control, but elsewhere, the theory was being developed in domains from physics to finance to winning a theological argument (really!). Jordan tells some part of this story and also gesture at ways that random walks (or Markov processes, named after the theological arguer) underlie current approaches to artificial intelligence; he touches on some of his own work with DeepMind and speculates about the capabilities of those systems now and in the future. Jordan Ellenberg is a Professor of Mathematics at the University of Wisconsin-Madison.

#Postdoc position on the science of science available at Indiana University. indiana.peopleadmin.com/postings/25173

I am looking for at least one #postdoc researcher with an initial appointment till March 2026, which may be extendable befond March 2026 (with a realistic probability). The work location is the State University of New York at Buffalo, Buffalo, NY, USA. The postdoc will be advised by Professor Naoki Masuda and housed in the Department of Mathematics (while the research itself is more data analytic, bioinformatic, and/or computational, rather than mathematical). The project has multiple subtopics, and priority areas are (i) gene network analysis and (ii) other (specific) biomedical data analysis related to network analysis (early warning, health behavior, etc.). The project is at intersection between network science and biology/biomedicine. Experience in bioinformatics in broad terms, genome biology, network science, data science, time series analysis, or related fields, and good communication skills are desired. If interested, please email to naokimas@gmail.com

2 year #Postdoc position with the option of extension. Deadline for application is August 16. The new Center for Synergy of Systems (SynoSys) develops transdisciplinary approaches and methods in the fields of network science, data science and citizen science in order to conduct integrative research into complex phenomena at the interfaces between biomedicine, social sciences and life sciences. https://www.verw.tu-dresden.de/StellAus/stelle.asp?id=11538&lang=en

Course on Complexity Economics Marco Pangallo’s course on Complexity Economics is part of the curriculum for the master’s degree in physics of complex systems at the University of Torino. The course aims to provide an interdisciplinary approach to understanding economic systems through the lens of complexity science. It covers basic methods and models used in complexity economics, focusing on the behavior and interactions of agents within economic systems. The course is designed to evolve into a comprehensive textbook on the subject, offering insights into non-mainstream, or heterodox, approaches to economics that leverage the tools and perspectives of complex systems science. http://www.marcopangallo.it/blog/2024/06/25/my-course-on-complexity-economics/

How America’s Fastest Swimmers Use Math to Win Gold? Number theorist Ken Ono is teaching Olympians to swim more efficiently. https://www.quantamagazine.org/how-americas-fastest-swimmers-use-math-to-win-gold-20240710/

What Does Machine Learning Have to Offer Mathematics? Jordan Ellenberg (University of Wisconsin–Madison) Monday, July 22, 2024 5:30 – 7 p.m. PT Simons Institute for the Theory of Computing Calvin Lab auditorium Please register to attend The interaction of machine learning with math has attracted a lot of attention, because mathematics is in some respects a closed world with well-defined rules (like chess, and unlike poetry-writing) but also a domain where success is ultimately judged by human assessments of ingenuity and importance, not rigid criteria (like poetry-writing, and unlike chess). Can machines prove theorems? Can they have mathematical ideas? Jordan Ellenberg will talk about his joint work with researchers from DeepMind, which used novel techniques in machine learning to make progress in a problem in combinatorics, and will try to chart some near-term ways that machine learning may affect mathematical practice. Jordan Ellenberg is a professor of mathematics at the University of Wisconsin–Madison specializing in number theory and arithmetic geometry, with additional interests in combinatorics, topology, and applications of machine learning to pure math. He is also the author of two general-audience books on mathematics, How Not to Be Wrong and Shape, as well as many articles in newspapers and magazines.

Media Complexity: Towards a Research Agenda to Study News Media Ecosystems Djordje Padejski, Arizona State University The talk will explore the applicability of complexity theory to journalism and mass communication studies in response to the digital transformation that has redefined journalistic practices, media narratives, and audience engagement. It will highlight the limitations of traditional research paradigms in capturing the complexity, nonlinearity, emergence, and interdependence of modern news media ecosystems. By integrating complexity theory, which focuses on adaptability, evolution, emergence, self-organization, and feedback mechanisms, among others, the talk intends to initiate a new research agenda. This includes examining the adaptability and evolution of media entities, the evolution of news narratives, the dynamics of media ecosystems, the diversity of media landscapes, self-organization in journalism, and the impact of audience feedback. The goal is to demonstrate the potential of complexity theory to offer novel insights into the evolving transformations in the journalism and mass communication field. https://youtu.be/ionT6lXjFG4

📣 2024 Permanent Positions at IFISC! Open call for two CSIC Tenured Scientist positions in "Cross-disciplinary Physics and Complex Systems" and "Theory of Quantum Technologies" https://ifisc.uib-csic.es/en/about-ifisc/join-us/ifisc-new-permanent-researcher-positions/

Science Communication in Network Science, Abbas K. Rizi I’m a Network Epidemiologist who moonlights as a science writer for the public. I mainly contribute to science communication as the editor-in-chief of sitpor.org (FA). Using my experiences, both in science writing and as a TA of different courses, I will reflect on the situation of science communication in the network science community. I’ll discuss the opportunities and materials severely lacking at all levels, such as the educational material for grad students and laypeople. 🎞 https://youtu.be/R4IDFcCnTrk ---------------------------------------------- @sitpor | sitpor.org instagram.com/sitpor_media #سیتپـــــور به خاطر روایتگری در علم

The Enduring Mystery of How Water Freezes Making ice requires more than subzero temperatures. The unpredictable process takes microscopic scaffolding, random jiggling and often a little bit of bacteria. https://www.quantamagazine.org/the-enduring-mystery-of-how-water-freezes-20240617/

Second Chance: Unorthodox but Personally Effective Explanations in Probability and Physics (Advanced undergraduate/early graduate level thermodynamics/statistical/quantum physics, with supplements on the mathematics required.) Blake C. Stacey https://www.sunclipse.org/wp-content/downloads/2024/06/statphys-book-incomplete.pdf

#PHD POSITION ON MATHEMATICAL STATISTICS AND MACHINE LEARNING THEORY https://utwentecareers.nl/en/vacancies/1822/phd-position-on-mathematical-statistics-and-machine-learning-theory/

Peter Latham: Introduction to Neuroscience https://youtu.be/IvY5L2VJ-9E?list=PLonWNO9SywvJvvc_h-Q2-G7HiwoufuCJF

Matteo Marsili: Beyond pairwise models in statistical inference https://youtu.be/tBC-Q7fDRJk