Complex Systems Studies

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 Is Science Even Possible?

How are scientists able to crack fundamental questions about nature and life? How does math make the complex cosmos understandable? In this episode, the physicist Nigel Goldenfeld and co-host Steven Strogatz explore the deep foundations of the scientific process.

https://www.quantamagazine.org/how-is-science-even-possible-20240620/

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

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@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

Blake C. Stacey

https://www.sunclipse.org/wp-content/downloads/2024/06/statphys-book-incomplete.pdf

This is a book about heat and unpredictability. I am writing it during a pandemic, while humanity goes about destroying the Earth’s climate and our democratic institutions, flawed to begin with, verge upon splintering. I began this project during the first COVID spring because I was teaching statistical physics to graduate students and wanted to provide as much for them as I could. I continue it because teaching science feels at least a little important and, unlike so many important things, may not be entirely beyond my capabilities.

#PHD POSITION ON MATHEMATICAL STATISTICS AND MACHINE LEARNING THEORY

https://utwentecareers.nl/en/vacancies/1822/phd-position-on-mathematical-statistics-and-machine-learning-theory/

ising

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

#postdoc in the Department of Biostatistics at Harvard! This position involves developing statistical methods, data analytic tools, and mathematical models for analyzing smartphone data collected in biomedical research cohorts.

https://academicpositions.harvard.edu/postings/13728

A First Course in Monte Carlo Methods

https://arxiv.org/abs/2405.16359

This is a concise mathematical introduction to Monte Carlo methods, a rich family of algorithms with far-reaching applications in science and engineering. Monte Carlo methods are an exciting subject for mathematical statisticians and computational and applied mathematicians: the design and analysis of modern algorithms are rooted in a broad mathematical toolbox that includes ergodic theory of Markov chains, Hamiltonian dynamical systems, transport maps, stochastic differential equations, information theory, optimization, Riemannian geometry, and gradient flows, among many others. These lecture notes celebrate the breadth of mathematical ideas that have led to tangible advancements in Monte Carlo methods and their applications. To accommodate a diverse audience, the level of mathematical rigor varies from chapter to chapter, giving only an intuitive treatment to the most technically demanding subjects. The aim is not to be comprehensive or encyclopedic, but rather to illustrate some key principles in the design and analysis of Monte Carlo methods through a carefully-crafted choice of topics that emphasizes timeless over timely ideas. Algorithms are presented in a way that is conducive to conceptual understanding and mathematical analysis -- clarity and intuition are favored over state-of-the-art implementations that are harder to comprehend or rely on ad-hoc heuristics. To help readers navigate the expansive landscape of Monte Carlo methods, each algorithm is accompanied by a summary of its pros and cons, and by a discussion of the type of problems for which they are most useful. The presentation is self-contained, and therefore adequate for self-guided learning or as a teaching resource. Each chapter contains a section with bibliographic remarks that will be useful for those interested in conducting research on Monte Carlo methods and their applications.

The Future of Scientific Computing: A Global Perspective

Leading scientists, policymakers, and industry experts from around the world will gather at ICTP's International Symposium to discuss the opportunities offered by scientific computing in tackling key scientific challenges, with a global perspective. The symposium will explore how artificial intelligence, high-performance computing and recent advancements in quantum computing will impact the scientific discovery process across scientific fields, including climate science, molecular chemistry, and materials science. Reflecting ICTP's core mission, the symposium will emphasize a global and inclusive approach that can address the needs of scientific communities working in the world's less developed regions. The symposium is part of ICTP's 60th anniversary celebrations taking place throughout 2024, culminating in a high-level event in November 2024. Outcomes of the symposium will be presented and discussed during a panel session at this November event.
https://youtu.be/0Uqnh-_MRB8

#PhD TA position in algorithms for complex networks at Eindhoven University of Technology. The position is for 5 years with 25% in teaching. Application dead-line June 16.

https://jobs.tue.nl/en/vacancy/phd-ta-on-algorithms-for-complex-networks-1079006.html

The Incomplete Guide to the Art of Discovery

Cornell emeritus professor Jack Oliver draws on his nearly 50 years as a scientist to explore the strategies, tactics, and personal traits and attitudes necessary for fruitful scientific discovery.