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

پست‌داک در دانشگاه کردستان
https://sbna.uok.ac.ir/postdoctoral-opportunity-in-network-analysis-at-sbna-lab.html

#postdoc Researcher positions | Statistical Physics and Active Matter

https://www.mpg.de/25427964/postdoctoral-researcher-positions-mpids-w088

Deep Learning Techniques for the Analysis of Soccer Matches

Pegah Rahimian

Postdoctoral position at Department of Information Technology; Division of Systems and Control

Zoo of Centralities: Encyclopedia of Node Metrics in Complex Networks

Centrality is a fundamental concept in network science, providing critical insights into the structure and dynamics of complex systems such as social, transportation, biological and financial networks. Despite its extensive use, there is no universally accepted definition of centrality, leading to the development of a large variety of distinct centrality measures. These measures have grown so numerous that they resemble a 'zoo', each representing a unique approach to capturing node importance within a network. However, the increasing number of metrics being developed has led to several challenges, including issues of discoverability, redundancy, naming conflicts, validation and accessibility. This work aims to address these challenges by providing a comprehensive catalog of over 400 centrality measures, along with clear denoscriptions and references to original sources. While not exhaustive, this compilation represents the most extensive and systematic effort to date in organizing and presenting centrality measures. We also encourage readers to explore and contribute to the Centrality Zoo website at this https URL, which provides an interactive platform for discovering and comparing centrality measures.

https://arxiv.org/abs/2511.05122

epydemix is an open-source Python package designed for flexible, modular, and data-driven epidemic modeling.

https://www.epydemix.org/

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013735

Epistorm-Mix provides privacy-preserving contact data and contact patterns characterization relevant for the spread of respiratory infectious diseases within the US population.

Built on a probability-based sample, Epistorm-Mix delivers population-level contact matrices and mixing indicators that reflect real-world heterogeneity across age, sex, race/ethnicity, income, and settings. These data enable epidemic models to better capture differential infection risks and to test targeted interventions in silico—offering actionable insight for seasonal preparedness and pandemic response.
The Repository

https://www.epistorm.org/data/epistorm-mix

Roland Bauerschmidt - Perspectives on the renormalisation group approach

https://youtu.be/l1iZbsEiO6c

The Unreasonable Effectiveness of Spectral Graph Theory: A Confluence of Algorithms, Geometry, and Physics

https://youtu.be/8XJes6XFjxM

Round table: Ruminations on the Ising Model: Past, Present, Future

with:
Jürg Fröhlich (ETH Zürich)
Tom Spencer (IAS)
Arthur Jaffe (Harvard University)
Geoffrey Grimmett (University of Cambridge)
Joel Lebowitz (Rutgers University)

https://youtu.be/YvS0j2pj_xY

Sociophysics models inspired by the Ising model
https://arxiv.org/abs/2506.23837

The Ising model, originally developed for understanding magnetic phase transitions, has become a cornerstone in the study of collective phenomena across diverse disciplines. In this review, we explore how Ising and Ising-like models have been successfully adapted to sociophysical systems, where binary-state agents mimic human decisions or opinions. By focusing on key areas such as opinion dynamics, financial markets, social segregation, game theory, language evolution, and epidemic spreading, we demonstrate how the models describing these phenomena, inspired by the Ising model, capture essential features of collective behavior, including phase transitions, consensus formation, criticality, and metastability. In particular, we emphasize the role of the dynamical rules of evolution in the different models that often converge back to Ising-like universality. We end by outlining the future directions in sociphysics research, highlighting the continued relevance of the Ising model in the analysis of complex social systems.

This graph will change how you see the world

https://youtu.be/HBluLfX2F_k?si=YhdsUwhrulPb2JxW