Complex Systems Studies

CSS, recognize the genocide in Gaza! This is a call to all members of the Complex Systems Society. We ask the CSS to join other academic organisations in publicly condemning the ongoing genocide in Gaza. We ask you to sign the letter below in support of the initiative. https://docs.google.com/forms/d/e/1FAIpQLSc9yqD3JufuaNBJQkg7q0KsYqnUSpYnRVprbb8E-V7rV7To2A/viewform

We are hiring multiple #PhD and #postdoc researchers for two newly funded projects related to the interaction of mental health and political polarization. The positions are in the Department of Computer Science at Aalto University in Finland. You will be joining a larger group of researchers working on similar topics. The department has a strong community of researchers working on related topics, including digital health and welbeing, network science, computational social science, and many topics in machine learning. https://www.aalto.fi/en/open-positions/open-postdoctoral-and-doctoral-positions-to-work-on-polarization-and-mental-health

The Era of Experience & The Age of Design: Richard S. Sutton, Upper Bound 2025 https://youtu.be/FLOL2f4iHKA Welcome to the era of experience https://storage.googleapis.com/deepmind-media/Era-of-Experience%20/The%20Era%20of%20Experience%20Paper.pdf

#Postdoc in Computational Social Science https://liu.se/en/work-at-liu/vacancies/27207

منظور ما از پدیدارگی یا emergence در سیستم‌های پیچیده چیه؟! انگاره پیچیدگی عینک جدیدی برای مطالعه طبیعت به ما می‌دهد. سیستم‌های پیچیده از تعداد زیادی اجزا تشکیل شده‌اند که در مقیاس ریز، اجزایشان برهم‌کنش‌های موضعی دارند و در مقیاس درشت، رفتارهای «پدیداره» از خود نشان می‌دهند که شبیه به رفتار اجزای آن‌ها در مقیاس ریز نیست. پدیدارگی در مورد این جور پدیده‌هاست. این ویدیو دعوتی است برای خواندن این مقاله مروری کوتاه: What is emergence, after all? 🔗 https://arxiv.org/abs/2507.04951 🎞 https://youtu.be/fMyuRjgFu-I 🎧 Audio File ---------------------------------------------- @sitpor  |  sitpor.org instagram.com/sitpor_media

#Postdoc Research Assistant in Machine Learning Statistics, 24-29 St Giles’, Oxford, OX1 3LB We invite applications for a full-time Postdoctoral Research Associate to join the new Data-Driven Algorithms for Data Acquisition (DataAcq) project. This is a timely project developing new methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon Guarantee for an ERC Starting Grant awarded to Prof Tom Rainforth. The post holder will undertake innovative research as part of the RainML Lab (https://www.rainml.uk/) towards the goals of the DataAcq project. In particular, the post holder will be expected to undertake research related to one or more of the three work packages of the project: a) scalable and robust Bayesian experimental design, b) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models). The successful postholder will hold or be close to the completion of a PhD/DPhil in Machine Learning, Statistics, Computer Science or closely related discipline. They will demonstrate an ability to publish, including the ability to produce high-quality academic writing. They will have the ability to contribute ideas for new research projects and research income generation. Previous research experience in one or more areas relevant to the research programme. For example: probabilistic machine learning, deep learning, experimental design, active learning, generative modelling, computational statistics, reinforcement learning, or Bayesian optimisation. This must include the ability to develop and/or analyse new methodology. Proficiency in the use of PyTorch, Tensorflow, Jax, or an equivalent deep learning library is desirable. We proudly hold a Race Equality Charter Bronze Award and a departmental Athena SWAN Silver Award, which guide our progress towards advancing racial and gender equality. Applicants will be selected for interview purely based on their ability to satisfy the selection criteria as outlined in full in the job description. You will be required to upload a statement setting out how you meet the selection criteria, a curriculum vitae, and the contact details of two referees as part of your online application. Please note that applicants are responsible for contacting their referees and making sure that their letters are sent to hr@stats.ox.ac.uk directly by the closing date. Please direct informal enquiries about the post to Professor Tom Rainforth rainforth@stats.ox.ac.uk, quoting vacancy reference 181060. Only applications received before 12.00 noon UK time on 03 September 2025 can be considered. Interviews are anticipated to be held on 24 September 2025. Link: https://www.jobs.ac.uk/job/DOC113/postdoctoral-research-assistant-in-machine-learning

#Postdoc (Bioinformatics/Data Science) in data-driven protein-protein interaction research at Department of Drug Design and Pharmacology https://jobportal.ku.dk/videnskabelige-stillinger/?show=164645

The Scales of Human Mobility - Laura Alessandretti https://youtu.be/AwKlne2VAME

Elements of successful NIH grant applications https://www.pnas.org/doi/10.1073/pnas.2315735121

Optimistic people are all alike: Shared neural representations supporting episodic future thinking among optimistic individuals https://www.pnas.org/doi/10.1073/pnas.2511101122 Neural processing of cognitive function is similar among individuals with positive traits but more dissimilar among those with negative traits. Applying the cross-subject neural representational analytical approach, we found that optimistic individuals display similar neural processing when imagining the future, whereas less optimistic individuals show idiosyncratic differences. Additionally, we found that optimistic individuals imagined positive events as more distinct from negative events than less optimistic individuals. Findings derived from a combination of IS-RSA and INDSCAL, suggest the existence of shared neurocognitive representations based on the emotional dimension among optimistic individuals, despite the fact that different individuals may perceive the same future event differently.

Estimated fraction of LLM-modified sentences across research paper venues over time. https://www.nature.com/articles/s41562-025-02273-8

Human Mobility in Epidemic Modeling Human mobility forms the backbone of contact patterns through which infectious diseases propagate, fundamentally shaping the spatio-temporal dynamics of epidemics and pandemics. While traditional models are often based on the assumption that all individuals have the same probability of infecting every other individual in the population, a so-called random homogeneous mixing, they struggle to capture the complex and heterogeneous nature of real-world human interactions. Recent advancements in data-driven methodologies and computational capabilities have unlocked the potential of integrating high-resolution human mobility data into epidemic modeling, significantly improving the accuracy, timeliness, and applicability of epidemic risk assessment, contact tracing, and intervention strategies. This review provides a comprehensive synthesis of the current landscape in human mobility-informed epidemic modeling. We explore diverse sources and representations of human mobility data, and then examine the behavioral and structural roles of mobility and contact in shaping disease transmission dynamics. Furthermore, the review spans a wide range of epidemic modeling approaches, ranging from classical compartmental models to network-based, agent-based, and machine learning models. And we also discuss how mobility integration enhances risk management and response strategies during epidemics. By synthesizing these insights, the review can serve as a foundational resource for researchers and practitioners, bridging the gap between epidemiological theory and the dynamic complexities of human interaction while charting clear directions for future research. https://www.arxiv.org/abs/2507.22799

#PhD studentships in real-time infectious disease modelling https://www.lshtm.ac.uk/study/fees-and-funding/funding-scholarships/research-degree-funding/phd-studentships-real-time-infectious-disease-modelling

Integrating explanation and prediction in computational social science https://youtu.be/c7BB5Svd8aw?list=PLrDB6riLfdJQaATZksFnXsWflA2cea9We Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions—the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes—and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal. https://www.nature.com/articles/s41586-021-03659-0

Why AI chatbots lie to us | Science https://www.science.org/doi/10.1126/science.aea3922

Will AI outsmart human intelligence? - with 'Godfather of AI' Geoffrey Hinton https://youtu.be/IkdziSLYzHw

Opinion dynamics: Statistical physics and beyond https://arxiv.org/abs/2507.11521

Introduction to correlation networks: Interdisciplinary approaches beyond thresholding https://arxiv.org/abs/2311.09536