On the foundations of statistical mechanics
https://arxiv.org/abs/2411.08709

Two fully funded #PhD students and two Machines (SOHAM) at Trinity College Dublin

https://tahayasseri.com/join/

Science Writing Fellow, Quanta Magazine
Quanta Magazine is looking for an early-career science journalist for its spring 2025 writing fellowship. This six-month program will give the successful applicant extensive experience writing news and features about one or more areas that Quanta covers: physics, mathematics, biology and computer science.

https://simonsfoundation.wd1.myworkdayjobs.com/en-US/simonsfoundationcareers/job/Science-Writing-Fellow_R0001723

Where postdoctoral journeys lead?https://arxiv.org/abs/2411.03938

The prestige of a PhD-granting institution is well known for influencing faculty hiring and career progression in American academia. But what about the postdoc stage—what impact does it have on shaping academic careers globally?

Based on the data*, high-impact publications and mobility with a little twist over the research topic during the postdoc are key to enhancing your academic career. Surprisingly, the prestige of the institution you move to is less influential than expected. So, regardless of your success in PhD, your odds improve by making moderate shifts in research focus and location with significant shifts in publication quality.

*: The authors used a comprehensive dataset by combining publication records from the Microsoft Academic Graph (MAG) with career data from a major online professional network. Spanning 25 years and encompassing 45,572 careers from various disciplines, the dataset specifically examines the influence of postdoctoral experiences on early academic success—considering factors like relocation, topic change, and early high-impact publications.

#PhD position in Digital Chemistry

project on combining traditional expert features with deep learning for reaction prediction.
https://www.jobs.ethz.ch/job/view/JOPG_ethz_4etBDEk8lO4Z8W71Q8

Fast unfolding of communities in large networks: 15 years later
The Louvain method was proposed 15 years ago as a heuristic method for the fast detection of communities in large networks. During this period, it has emerged as one of the most popular methods for community detection: the task of partitioning vertices of a network into dense groups, usually called communities or clusters. Here, after a short introduction to the method, we give an overview of the different generalizations, modifications and improvements that have been proposed in the literature, and also survey the quality functions, beyond modularity, for which it has been implemented. Finally, we conclude with a discussion on the limitations of the method and perspectives for future research.

https://iopscience.iop.org/article/10.1088/1742-5468/ad6139

#PhD students at Berkeley:
if you are interested in ML applied to health, inequality, or social science, and mention Emma Pierson in your app.

More details on work/how to apply: https://cs.cornell.edu/~emmapierson/

From classical to modern opinion dynamicsHossein Noorazar, Kevin R. Vixie, Arghavan Talebanpour, Yunfeng Hu

In this age of Facebook, Instagram and Twitter, there is rapidly growing interest in understanding network-enabled opinion dynamics in large groups of autonomous agents. The phenomena of opinion polarization, the spread of propaganda and fake news, and the manipulation of sentiment are of interest to large numbers of organizations and people, some of whom are resource rich. Whether it is the more nefarious players such as foreign governments that are attempting to sway elections or large corporations that are trying to bend sentiment -- often quite surreptitiously, or it is more open and above board, like researchers that want to spread the news of some finding or some business interest that wants to make a large group of people aware of genuinely helpful innovations that they are marketing, what is at stake is often significant. In this paper we review many of the classical, and some of the new, social interaction models aimed at understanding opinion dynamics. While the first papers studying opinion dynamics appeared over 60 years ago, there is still a great deal of room for innovation and exploration. We believe that the political climate and the extraordinary (even unprecedented) events in the sphere of politics in the last few years will inspire new interest and new ideas. It is our aim to help those interested researchers understand what has already been explored in a significant portion of the field of opinion dynamics. We believe that in doing this, it will become clear that there is still much to be done.

https://arxiv.org/abs/1909.12089

Reality-inspired voter models: A mini-review
Sidney Redner

This mini-review presents extensions of the voter model that incorporate various plausible features of real decision-making processes by individuals. Although these generalizations are not calibrated by empirical data, the resulting dynamics are suggestive of realistic collective social behaviors.

https://www.sciencedirect.com/science/article/pii/S1631070519300325

How are people able to map knowledge from one domain to another? I'll report a series of studies showing that people's cross-domain mappings were best predicted by similarity along abstract dimensions such as valence, complexity, and genderedness - a finding that could be reliably simulated by language models. In an ongoing study, we further asked what allows people to process cross-domain mappings so easily by drawing insights from a network perspective.

https://www.youtube.com/watch?v=DkiCE8rBi9k