About Philip Warren Anderson

https://arxiv.org/abs/2510.20865

Shark Data Suggests Animals Scale Like Geometric Objects

https://www.quantamagazine.org/shark-data-suggests-animals-scale-like-geometric-objects-20251027/

Lake Como School: Complex Networks
Theory, Methods, and Applications - May 18-22, 2026

https://ntml.lakecomoschool.org/

Senior Scientist (= #PostDoc with option of permanency)!

https://jobs.uni-graz.at/en/jobs/7d1400f4-cbe6-ab69-dc82-68e77675d81c?category=Academic%20Staff%20(Non-Tenure-Track),Academic%20Staff%20(Development%20Position)

Towards an Understanding of Scientific Disagreement
https://vimeo.com/1131827105

🔺🔺🔺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!

Simon DeDeo, Hard Proofs and Good Reasons | Natural Philosophy Symposium 2025
https://youtu.be/JJLBZ4C1OGw

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

#Postdoc position for Generative models for molecular dynamics

https://www.chalmers.se/en/about-chalmers/work-with-us/vacancies/?rmpage=job&rmjob=14027&rmlang=UK

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/

Contact structure and population immunity shape the selective advantage of emerging variants.

Epidemics are shaped by the interplay between host and pathogen population characteristics, which are themselves intertwined. In particular, host behavior and immunity profile shape pathogen population structure by affecting both the likelihood of new variant emergence and its subsequent dynamics. Theoretical studies provided a fragmented description of this complex dynamical dependency, and the empirical evidence is limited. The SARS-CoV-2 pandemic presents an unprecedented opportunity to study the emergence of new variants. The relative growth of emerging variants over the resident ones, i.e., the selection coefficient, showed spatiotemporal variations that could be associated with population immunity and the mean and dispersion of contacts, which varied greatly according to epidemic intensity and human response. We first investigated the impact of these three features on the selection coefficient using a stochastic network-based model of new variant emergence, which incorporates tunable connectivity and heterogeneity. Results systematically chart the parameter space, uncover the boundaries of previously known associations, and quantify their strength. The mean number of contacts was positively associated with the selection coefficient, the effect being more robust for low immune-escape variants. The impact of immunity diminished as immunity increased. Importantly, greater contact dispersion slowed down the spread of variants lacking immune escape, but this effect quickly reversed once immune escape became non-zero. We then analysed the emergence of the SARS-CoV-2 Alpha variant in the United States at the state level, examining the association of the selection coefficient with the three population features under study, reconstructed from serological, vaccination, and contact survey data. Regression analyses revealed a strong effect of population characteristics. Comparing empirical trends with model predictions showed consistency and suggested that the selection coefficient was more affected by contact statistics than by immunity. These results shed light on how human population structure mediates variant dynamics and help interpret the heterogeneity observed in variant emergence.

https://www.medrxiv.org/content/10.1101/2025.11.07.25339691v1

A Conceptual Introduction to Hamiltonian Monte Carlo

Hamiltonian Monte Carlo has proven a remarkable empirical success, but only recently have we begun to develop a rigorous understanding of why it performs so well on difficult problems and how it is best applied in practice. Unfortunately, that understanding is confined within the mathematics of differential geometry which has limited its dissemination, especially to the applied communities for which it is particularly important. In this review I provide a comprehensive conceptual account of these theoretical foundations, focusing on developing a principled intuition behind the method and its optimal implementations rather of any exhaustive rigor. Whether a practitioner or a statistician, the dedicated reader will acquire a solid grasp of how Hamiltonian Monte Carlo works, when it succeeds, and, perhaps most importantly, when it fails.

https://arxiv.org/abs/1701.02434

من امکان جذب یک یا دو پژوهشگر در گروه خود در یک موسسه تحقیقاتی در مادرید در یکسال آینده دارم و این امکان را ترجیح میدهم در اختیار دانشجویان و مجققین جوان ایرانی قرار دهم.
شرط پایه داشتن تجربه و تخصص در شبیه سازی نورونی و تحلیل دینامیک نورونی و طبیعتا دانش برنامه نویسی و توانایی تحلیل ریاضی است. پروژه اصلی مد نظر من طراحی و گسترش یک مدل نورونی برای تشخیص الگوهای زمانی-مکانی و درک نقش نوسانات مغزی در این فرایند است.
این موقعیت ترجیحا در سطح پسادکتری جوان است برای کسانی که در سالهای اخیر (حدود سه سال یا کمتر) دکتری گرفته اند. ولی با شرایطی، برای پسادکتری باتجربه بیشتر در صورت داشتن سابقه خیلی خوب هم میتواند در نظر گرفته شود در صورتی که با پیشنهاد پروژه ای جذاب و در راستای کارهای من همراه باشد.
در شرایط خیلی خاص اگر دانشجوی برجسته ای هستید (نه نخبه با معیارهای رایج و تهی از معنای کنونی) و توانایی تحلیلی و برنامه نویسی بسیار خوبی دارید، و به دنبال موقعیت دکتری هستید هم با من تماس بگیرید.
در کنار شرایط علمی تخصصی، اگر تجربه و سابقه خوبی در فعالیت علمی در شبکه های اجتماعی و یا توانایی های فنی در تولید محتوا داشته باشید، و اگر بسیار خوب ساز میزنید (ترجیحا تار!) برای همکاری با من ارزشمند خواهد بود.

برای دیدن روند تحقیقاتی حتما صفحه اسکولار من را ببینید و خوب است صفحه اینستاگرام من را هم ببینید که با جنبه های دیگر همکاری احتمالی با من آگاه شوید (لینکهای انتهای پست).

برای من در کنار رزومه یا به جای آن، یک توصیف روایی از تواناییها و تجربه های خود بفرستید. ایمیل من را هم در اطلاعات تماس لینکدین میتوانید ببینید

https://www.linkedin.com/in/alireza-valizadeh-88867138/

Melanie Mitchell, Evaluating Cognitive Capacities in AI Systems | Natural Philosophy Symposium 2025
https://youtu.be/yKbeCvOKvMc?list=PLWDzKuETVzrpkaD32TBFn8sarleSfLJ3A

The pandemic next time
How President Donald Trump’s administration has undermined efforts to develop vaccines and drugs for the next viral scourge

https://www.science.org/content/article/trump-administration-dismantling-efforts-fight-next-pandemic

#PhD fellowship in Physics, Biocomplexity

https://employment.ku.dk/phd/?show=152510

سمینارهای هفتگی دانشکده‌ی فیزیک دانشگاه صنعتی خواجه نصیرالدّین طوسی:

پیچیدگی چیست؟
عباس ریزی

دوشنبه ۲۶ آبان‌ماه، ساعت ۱۲:۱۵ تا ۱۳:۳۰
سالن سفیر، پردیس شهید رضایی‌نژاد

https://meet.google.com/aec-wjdx-pqi
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@sitpor | sitpor.org
instagram.com/sitpor_media
#سیتپـــــور به خاطر روایتگری در علم

Chris Kempes, The Easy Part of Biology | Natural Philosophy Symposium 2025

https://youtu.be/riKfuUiGOjk

Bloomberg is pleased to announce the 2026-2027 edition of the Bloomberg Data Science #PhD Fellowship Program (https://www.techatbloomberg.com/bloomberg-data-science-ph-d-fellowship/), a premier initiative supporting outstanding Ph.D. students advancing the frontiers of data science, AI and their applications