two #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

#Postdoc Researcher on alignment of MMFM with ethical, legal, and social values. Specifically, Oxford Internet Institute

https://my.corehr.com/pls/uoxrecruit/erq_jobspec_version_4.display_form?p_company=10&p_internal_external=E&p_display_in_irish=N&p_process_type=&p_applicant_no=&p_form_profile_detail=&p_display_apply_ind=Y&p_refresh_search=Y&p_recruitment_id=180757

Strength and weakness of disease-induced herd immunity in networks
https://www.pnas.org/doi/10.1073/pnas.2421460122

What if #herd_immunity isn’t just about how many people are immune, but how they’re 'spatially' connected? Our new PNAS paper explores this concept. We show how the topology and geometry of social networks influence the dynamics of herd immunity, whether it arises from infection or #vaccination.

Here is a less technical blog post for a more general reader: abbas.sitpor.org/2025/07/10/the-spatial-puzzle-of-herd-immunity

Strength and Weakness of Disease-induced Herd Immunity in Networks

During the COVID-19 pandemic, several studies suggested that the spread of infection might induce herd immunity more easily than previously thought due to population heterogeneity. However, these studies relied on differential equation-based epidemic models, which cannot account for correlations between individuals. We reexamine the effect of disease-induced herd immunity using individual-based contact network models. We find that herd immunity is weaker when such correlations are taken into account, so much so that the conclusions of the previous studies may be overturned. This effect is especially pronounced when the contact network is spatially embedded. Our results highlight the importance of considering network effects in policy decisions that affect the lives and well-being of millions in future pandemics.

Generated by Google NotebookLM

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

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

Will AI outsmart human intelligence? - with 'Godfather of AI' Geoffrey Hinton

https://youtu.be/IkdziSLYzHw

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

This playlist contains all keynotes from IC2S2'25 in Norrköping, Sweden.

https://youtube.com/playlist?list=PLrDB6riLfdJQaATZksFnXsWflA2cea9We&si=JfZHBovx27npBd5t

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

#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

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

Estimated fraction of LLM-modified sentences across research paper venues over time.

https://www.nature.com/articles/s41562-025-02273-8

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.

Elements of successful NIH grant applications

https://www.pnas.org/doi/10.1073/pnas.2315735121

5 postulates for Successful Applications 101:

1. The application is for the reviewer, not you, the applicant—remember that.
2. Learn from the Greek—communicate in stories.
3. Your Specific Aims story needs to be cohesive—leave no puzzling gaps.
4. Motivate the reviewer to keep reading—make your story resonate.
5. There is serendipity and noise in the peer-review system—accept that.

The Scales of Human Mobility - Laura Alessandretti

https://youtu.be/AwKlne2VAME

#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

#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

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
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