Complex Systems Studies

علوم اجتماعی محاسباتی چیه؟ آینده‌ش چه جوریه؟ علوم اجتماعی محاسباتی (Computational Social Science) یک شاخه جدید و میان‌رشته‌ای است که علاوه بر روش‌های سنتی مانند نظرسنجی‌ها، از ابزارهای محاسباتی و داده‌هایی مثل فعالیت‌های آنلاین، شبکه‌های اجتماعی و اطلاعات دیجیتال برای مطالعه و تحلیل رفتارها و روابط اجتماعی استفاده می‌کند. این علم به ما کمک می‌کند تا الگوهای پیچیده در جوامع انسانی را بهتر بفهمیم، رفتارهای گروهی را پیش‌بینی کنیم، و تأثیر سیاست‌ها یا رویدادهای مختلف را بر جامعه ارزیابی کنیم. به طور خلاصه، علوم اجتماعی محاسباتی ترکیبی از داده‌ها، فناوری و تحلیل‌های اجتماعی است تا تصویری دقیق‌تر از جهان اجتماعی ارائه دهد. 🎞 ویدیو در یوتیوب 🎧 فایل صوتی ---------------------------------------------- @sitpor | sitpor.org instagram.com/sitpor_media #سیتپـــــور به خاطر روایتگری در علم

#postdoc in biophysics https://tuni.rekrytointi.com/paikat/index.php?jid=2417&key=&o=A_RJ&rspvt=8naa58dv9pk4sws8c80osowcsg4gg0g

#Master's in Physics of Complex Systems, offered by IFISC https://www.uib.eu/Learn/estudis-de-master/master/MFS3/index.html

Thermodynamics-inspired explanations of artificial intelligence https://www.nature.com/articles/s41467-024-51970-x

#Postdoc Research Associate - Computational Social Science https://northeastern.wd1.myworkdayjobs.com/careers/job/Boston-MA-Main-Campus/Postdoctoral-Research-Associate---Computational-Social-Science_R127070

Social networks, 1 PhD and 1 postdoc position available in Copenhagen. Deadline 6th October. More info: #PhD: https://employment.ku.dk/phd/?show=162456 #Postdoc: https://employment.ku.dk/faculty/?show=162457

We are looking for both theorists and experimentalists ready for a bit more independence than a regular #postdoc. To learn more: https://biophysics.princeton.edu/opportunities

#postdoc positions a) online social media and disinformation b) human mobility. https://jobs.fbk.eu/Annunci/Offerte_di_lavoro_Two_positions_for_Researchers_in_the_field_of_Analysis_and_Modelling_of_Human_Behaviour_from_Digital_Traces_232371974.htm

#Postdoc Fellow: Molecular Modelling & Machine Learning Gothenburg, Sweden https://careers.astrazeneca.com/job/gothenburg/postdoctoral-fellow-molecular-modelling-and-machine-learning/7684/69196037264

🦬 Faculty positions open at CU Boulder's BioFrontiers Institute! https://jobs.colorado.edu/jobs/JobDetail/?jobId=57065 - focus areas include parasites & pathogens, comp bio - tenure home in dept that fits best for you - wonderful environment & culture - multiple Asst. Prof hires expected

Swiss Government Excellence Scholarships at #phd and #PostDoc Levels for research or study at Swiss universities and research/techology institutes. https://www.sbfi.admin.ch/sbfi/en/home/education/scholarships-and-grants/swiss-government-excellence-scholarships.html#632937668

If you're interested in working with me and you’re one of the rare folks who meet these requirements and still want to stick around in academia, you might join me as a #postdoc in Madrid! - You have a PhD. - You’ve got a solid understanding of neural dynamics and good experience with neural modeling. - You’ve worked with EEG or brain stimulation.

The Potential for AI in Science and Mathematics - Terence Tao https://youtu.be/_sTDSO74D8Q

#PhD opportunity in the excellent dynamics group https://euraxess.ec.europa.eu/jobs/265393

#PhD student interested in combining social network research with analysis of digital trace data in the domain of music tastes and cultural consumption! https://sn.ethz.ch/

A Deep Dive into Aalto ScienceIT Project: An Interview with Richard Darst 🎞 Video on YouTube 🎧 Audio Richard Darst from Aalto Science IT discusses the transition from chemistry to network science, emphasizing the importance of software engineering support & practical training for researchers. He explains the development and benefits of Aalto’s Triton HPC cluster and JupyterHub, and how his team assists researchers with essential computational tools and user-friendly training programs. A must-watch for anyone interested in scientific computing and interdisciplinary collaboration. • The Aalto Research Software Engineers (RSEs) • CodeRefinery Project • Triton HPC Cluster • Aalto Science IT Services 🔗 Don’t forget to like, comment, and subscribe for more: youtube.com/AbbasKRizi ---------------------------------------------- @sitpor | sitpor.org instagram.com/sitpor_media #سیتپـــــور به خاطر روایتگری در علم

In the process of building the new Center Synergy of Systems (SynoSys), (https://synosys.github.io) at Technische Universität Dresden we are looking for a data visualization specialist to develop interactive, web-based visualizations of our research. If you share our interest of anti-disciplinary research and bring the required skills, have a look at the job post and apply. https://lnkd.in/e9Zh3F4g

2025 Complexity #Postdoc Fellowships. Complexity Fellows contribute to SFI’s research and collaborate with leading researchers worldwide. If you’ve completed your PhD in any scientific discipline, and are interested in transdisciplinary research, consider this unique opportunity at the Santa Fe Institute. Applications are open until October 11 – check out application requirements and benefits here: https://apply-sfi.smapply.org/prog/complexity_postdoctoral_fellowship_/

Statistical Laws in Complex Systems Eduardo G. Altmann Statistical laws describe regular patterns observed in diverse scientific domains, ranging from the magnitude of earthquakes (Gutenberg-Richter law) and metabolic rates in organisms (Kleiber's law), to the frequency distribution of words in texts (Zipf's and Herdan-Heaps' laws), and productivity metrics of cities (urban scaling laws). The origins of these laws, their empirical validity, and the insights they provide into underlying systems have been subjects of scientific inquiry for centuries. This monograph provides an unifying approach to the study of statistical laws, critically evaluating their role in the theoretical understanding of complex systems and the different data-analysis methods used to evaluate them. Through a historical review and a unified analysis, we uncover that the persistent controversies on the validity of statistical laws are predominantly rooted not in novel empirical findings but in the discordance among data-analysis techniques, mechanistic models, and the interpretations of statistical laws. Starting with simple examples and progressing to more advanced time-series and statistical methods, this monograph and its accompanying repository provide comprehensive material for researchers interested in analyzing data, testing and comparing different laws, and interpreting results in both existing and new datasets. https://arxiv.org/abs/2407.19874