Complex Systems Studies
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#complexity #complex_systems #networks #network_science

📨 Contact us: @carimi
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#PhD opportunity to predict how natural populations will respond to perturbations:
https://www.findaphd.com/phds/project/social-manipulations-for-predicting-wild-animal-societies-responses-to-perturbations/?p187979

The project will combine large-scale social data from model animal systems with network analyses and social manipulations to understand the causal effects of external forces on real-world societies.
Application Deadline 7 Jan 2026, fully funded through YES-DTN scheme at University of Leeds.
#phd Developing methods for accurate reconstruction of viral histories to guide pandemic preparedness and targeted interventions
https://www.ndm.ox.ac.uk/study/dphil-themes?project=developing-methods-for-accurate-reconstruction-of-viral-histories-to-guide-pandemic-preparedness-and-targeted-interventions
🔺🔺🔺Postdoc Opportunity – Electrophysiology Lab (Shahid Beheshti University)

Dr. Reza Lashgari is looking for postdoctoral researchers to join his electrophysiology lab at Shahid Beheshti University.
Candidates with a strong background in neuroscience, signal recording, and signal processing are encouraged to apply.

Please share this with anyone who might be interested!
Reticula: A temporal network and hypergraph analysis software package

Abstract: In the last decade, temporal networks and static and temporal hypergraphs have enabled modelling connectivity and spreading processes in a wide array of real-world complex systems such as economic transactions, information spreading, brain activity and disease spreading. Here, we present the Reticula C++ library and Python package: A comprehensive suite of tools for working with real-world and synthetic static and temporal networks and hypergraphs. This includes various methods of creating synthetic networks and randomised null models based on real-world data, calculating reachability and simulating compartmental models on networks. The library is designed principally on an extensible, cache-friendly representation of networks, with an aim of easing multi-thread use in the high-performance computing environment.

In terms of challenges, I will talk more generally about the good and bad parts of writing and distributing software by scientists for scientists. What kind of skills would be useful? How can a PhD candidate reconcile scientific software development with the classic expectation of publishing papers and getting citations?

Speaker: Arash Badie-Modiri

https://youtu.be/k_psA5l07zQ
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First, I’ll re-introduce Metropolis-Hastings, the algorithm behind Gibbs Sampling and similar Markov chain algorithms. I assume most readers have at least heard of it. Instead of mathematical rigor, I’ll animate the approach so that the reader can appreciate both why it works and why it cannot scale with our inferential ambitions. Second, I’ll introduce Hamiltonian Monte Carlo, a very different approach to constructing Markov chains. Again, the goal is not to be mathematically precise, but to animate the algorithm and show why it works and why it still has limits.

https://elevanth.org/blog/2017/11/28/build-a-better-markov-chain/
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