Complex Systems Studies

MIT's class on Machine Learning in Healthcare is now available for free on MIT's OpenCourseWare! All videos, slides, and lecture notes can be found here: https://t.co/2WN7UZYMuU

💰 I'll be supporting a Gov Ireland #Postdoc Fellow (€45,895 annu.) application on "sociology of humans & machines" focus on hybrid intelligence, machine behaviour, algorithmic matchmaking... If interested, email me by 1 Nov with (CV+short statement) https://t.co/opgLeEHsOE Start date: 1 October 2021! Your PhD should be dated between 31 May 2016 and 31 May 2021.

100+ AI related courses include Bayesian data analysis, machine learning, computer vision, bioinformatics, deep learning, robotics, optimization, signal processing, speech recognition, Bayesian filtering and smoothing, HCI, distributed data infrastructures, and more https://fcai.fi/news/2020/10/13/large-selection-of-ai-related-courses-open-up-new-possibilities-for-students-from-diverse-disciplines

ICTP has announced postdoctoral opportunities in three of its research sections, starting in 2021: ☄️ High Energy, Cosmology and Astroparticle Physics ♾️ Mathematics ⚛️ Condensed Matter and Statistical Physics You can find more details here: https://t.co/HZdyO3aNgN #postdoc

ICTP-EAIFR Colloquium on "Machine learning and molecular dynamics" Speaker: Michele Parrinello (ETH-Z, USI Lugano, IIT Genoa) Atom based computer simulation is one of the most important tools of contemporary physical chemistry. In spite of its many successes, it suffers from severe limitations. Here we show how machine-learning techniques can help in solving at least two different problems. The first one is the accuracy of current interatomic potential models; the second is the limited time scale that simulations can explore. In order to solve the first problem we train a neural network on a set of accurate but expensive quantum chemical calculations. In this way, it is possible to obtain an accurate description of the system at a relatively low computational cost. Crucial for the success of this program has been the design of the neural work and the selection of the training set. We apply this approach to study a metal non-metal transition and to chemical reactions in condensed phases. These applications would not have been possible without the use of efficient sampling methods capable of lifting the time scale barrier. To this effect, we have developed two very efficient sampling methods, metadynamics and variationally enhanced sampling. Both methods are based on the identification of appropriate collective variables, or slow modes, whose sampling needs to be accelerated. Machine learning can be used also for the construction of efficient collective variables based on a modification of the well-known linear discriminant analysis classification method. Finally, we use the variational enhanced sampling approach and a deep neural network to further increase our sampling ability. 🎞 https://youtu.be/cAhn4Z3Rv9M?t=472

🦠 https://www.nature.com/articles/d41586-020-02948-4

💰 Do you want to get a #PhD working at the interface of statistical physics, Bayesian inference and machine learning? 📢 We are offering a fully-funded doctoral grant! ✉️ The application deadline is Oct 27 so contact us NOW if you are interested! https://t.co/T4A1Glig4q

Are you a young physicist or mathematician looking for a life-changing opportunity? ICTP is now accepting #applications for the 2021-2022 class of its Postgraduate #Diploma Programme! Click here to find out more and apply online 👉 https://t.co/wLuULx9Vog #complex_systems #physics #math

Are you a young physicist or mathematician looking for a life-changing opportunity? ICTP is now accepting #applications for the 2021-2022 class of its Postgraduate #Diploma Programme! Click here to find out more and apply online 👉 https://t.co/wLuULx9Vog #science #physics #math

💰 A number of attractive #PhD stipends are available in STAT at UCPH, including one on “Causal learning and hybrid causal model structures for event data”. Deadline Nov. 15. https://t.co/UdzqAuJMs7

💰 I'm hiring 2 #PhD students and 1 #Postdoc at Complexity Science Hub in Vienna starting from March 2021. Please spread the word. #CSS @CSHVienna https://t.co/xyr8M7C0u4 https://t.co/ELrYycXuZ5 https://t.co/rNGXniSP9B

💰 IFISC offers a new #PhD position, funded by the @ERC_Research through the ERC Starting Grant ARCTIC, on large-scale data processing in air transport. The position will remain open until filled. ℹ: https://t.co/3VOAxm97vB

Isotopy and energy of physical networks Yanchen Liu, Nima Dehmamy & Albert-László Barabási https://www.nature.com/articles/s41567-020-1029-z Abstract While the structural characteristics of a network are uniquely determined by its adjacency matrix1,2,3, in physical networks, such as the brain or the vascular system, the network’s three-dimensional layout also affects the system’s structure and function. We lack, however, the tools to distinguish physical networks with identical wiring but different geometrical layouts. To address this need, here we introduce the concept of network isotopy, representing different network layouts that can be transformed into one another without link crossings, and show that a single quantity, the graph linking number, captures the entangledness of a layout, defining distinct isotopy classes. We find that a network’s elastic energy depends linearly on the graph linking number, indicating that each local tangle offers an independent contribution to the total energy. This finding allows us to formulate a statistical model for the formation of tangles in physical networks. We apply the developed framework to a diverse set of real physical networks, finding that the mouse connectome is more entangled than expected based on optimal wiring.

The first Young Talent Symposium of the Dutch chapter of @netscisociety is coming up on November 2nd (3.30-5pm CET)! Sign up for this free virtual event at https://t.co/VswWJx9WTm

سرطان به عنوان یکی از بیماری‌های که این روزها نامش بر سرزبان‌ها افتاده است، نامی است که به مجموعه‌ای از بیماری‌هایی اطلاق می‌شود که از تکثیر مهارنشده سلول‌ها پدید می‌آیند. در مورد ژن‌ها می‌دانیم که بیان هر ژن بر بیان سایر ژن‌ها اثر می‌گذارد و وجود این همبستگی‌ها سبب تشکیل یک حرکت جمعی می‌شود که خود باعث اثر گذاشتن روی بیان سایر ژن‌ها می‌‌شود. هدف این مطالعه، نگاهی پدیدارشناسانه به سرطان سینه و مقایسه رفتار جمعی ژن‌ها در نمونه سالم و سرطانی است. با در نظر گرفتن سلول به عنوان یک سیستم پیچیده، می‌خواهیم شبکه پیچیده‌ای که در پس این سیستم نشسته است را مورد مطالعه قرار دهیم به امید این‌ که درک بهتری از سرطان از نگاه پیچیدگی پیدا کنیم. برای جزئیات بیشتر نگاه کنید به: Cite as: arXiv:2010.05897 ————————— ccnsd.ir/cancer مرکز شبکه‌های پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی @ccnsd

Join us virtually at Santa Fe Institute @sfiscience next May for the second annual Workshop on Stochastic Thermodynamics (WOST II), with a great line-up of invited speakers and prior tutorial sessions. https://t.co/rZpvHTBgQo

🦠 Scientists from Oxford Physics have developed an extremely rapid diagnostic test for Covid-19 that detects and identifies viruses in less than five minutes. Read more here > https://t.co/aY7ubgPz3G

💸 https://www.nature.com/articles/d41586-020-02875-4

I am continuing to recruit participants for my dissertation research! If interested please complete the following short Qualtrics survey (We plan to reach out to eligible participants within the next week!): https://t.co/We3YlZvjGv have an android smartphone and a Windows or Mac computer, are over the age of 18, and are fluent in English. Participants will receive $80 in amazon gift cards for their complete participation in the study. We plan to reach out to eligible participants within the next week!