Workshop: Machine Learning Glassy Dynamics

Amorphous materials such as glasses have been used since prehistoric times, but in recent years their technological applications have rapidly grown. However, despite many decades of research using experiments, theory and computer simulations, fundamental mechanisms such as mechanical properties and stress relaxation in glasses are still poorly understood. In recent years, machine learning techniques have started to be applied to help solve these fundamental questions, creating a new research path to study disordered materials. This workshop is dedicated to understanding the role that machine learning can play in better understanding glasses from a fundamental perspective. The original format should trigger new collaboration work between different actors and help organise this rapidly growing research field.

https://www.cnrs.fr/en/workshop-machine-learning-glassy-dynamics

🎞 https://www.youtube.com/playlist?list=PLLusiyVUCDJnprBuih5yDgToIzpsIdCi4

A journey from Network Medicine to understanding how food affects our health and diseases or the #Foodome project
A. Barabsi

https://youtu.be/FNfrp4giFlI

Open #PhD and #postdoc position in my soon-to-be newly established quantum information research group at Lund University, Sweden.

#job duration: 5 years for PhD and 2 years for postdoc. Starting in the first half of 2023.

Expertise is welcomed in any of the following topics: quantum correlations, Bell inequalities, entanglement, quantum communication, quantum key distribution, convex optimization, programming, quantum optics and open quantum systems.

PhD: https://lnkd.in/eRYSdF3h
Postdoc: https://lnkd.in/e3r5WGcr

Message passing methods on complex networks
Review article by M. E. J. Newman

Networks and network computations have become a primary mathematical tool for analyzing the structure of many kinds of complex systems, ranging from the Internet and transportation networks to biochemical interactions and social networks. A common task in network analysis is the calculation of quantities that reside on the nodes of a network, such as centrality measures, probabilities, or model states. In this review article we discuss message passing methods, a family of techniques for performing such calculations, based on the propagation of information between the nodes of a network. We introduce the message passing approach with a series of examples, give some illustrative applications and results, and discuss the deep connections between message passing and phase transitions in networks. We also point out some limitations of the message passing approach and describe some recently-introduced methods that address these limitations.

https://arxiv.org/abs/2211.05054

“Python for scientific computing” an on-line course aimed to improve your scientific Python skills starting Tuesday 22nd November at 10:00 EET (4 days, 3 hours per day). 1 ECTS available if you need it.

More info and registration at:
https://scicomp.aalto.fi/training/scip/python-for-scicomp-2022/

This course isn’t designed to teach Python itself, but if you know some Python, you will get a good introduction to the broader Python for science ecosystem and be able to use the right tools for your work and keep your code in good shape.

🌊Do you want to spend your next summer at the Baltic sea?
📈Are you studying data science or population science?
Apply for our Summer Research Visit in the Lab of Digital and Computational Demography at our Institute!

https://www.demogr.mpg.de/en/career_6122/jobs_fellowships_1910/population_and_social_data_science_summer_incubator_program_11474

Landau theory for the Mpemba effect through phase transitions
Roi Holtzman & Oren Raz

The Mpemba effect describes the situation in which a hot system cools faster than an identical copy that is initiated at a colder temperature. In many of the experimental observations of the effect, e.g. in water and clathrate hydrates, it is defined by the phase transition timing. However, none of the theoretical investigations so far considered the timing of the phase transition, and most of the abstract models used to explore the Mpemba effect do not have a phase transition. We use the phenomenological Landau theory for phase transitions to identify the second order phase transition time, and demonstrate with a concrete example that a Mpemba effect can exist in such models.

https://www.nature.com/articles/s42005-022-01063-2

#Job

https://www.jobbnorge.no/en/available-jobs/job/234196/associate-professor-in-computational-porous-media-physics

We are looking for a #postdoc researcher to work at the interface between Network Geometry and Machine Learning (dimension reduction techniques and neural networks).

http://bit.ly/3TPc9Xk

#PhD in Network Science

Developing methods to deal with uncertain time-evolving network data in order to enable more effective study of complex systems within and around us from our cellular biology to our social interactions

https://www.academictransfer.com/en/320231/phd-in-network-science/

Predicting a whole World Cup is hard. Predicting one match should be easier.

So how about England v Iran? What does Joshua Bull's model say?


https://youtu.be/KjISuZ5o06Q

We know you want to learn some useful Python so we offer Python for Scientific Computing course to everyone in the world.

The course is ongoing right now online and you can access material anytime: https://aaltoscicomp.github.io/python-for-scicomp/

I’m looking to hire a #PhD student to work broadly on machine learning, quantum simulation, strongly correlated many body systems (we only do cool stuff so it will be fun I promise)— if you’re interested or know somebody who might be, send me a message/email!

https://sites.google.com/view/annabelle-bohrdt?pli=1

The fourth special issue of JSTAT on the Statistical Physics aspects of Machine Learning/Artificial Intelligence has been published and it is now available at
https://iopscience.iop.org/collections/1742-5468_extraspecial20

A very nice collection of papers !

Predicting personality, death, emigration, and other life-events from embeddings of registry data

Sune Lehmann
December 12 , 11:00 – 12:00 UTC+2

https://www.popnet.io/event/popnet-connects-with-sune-lehmann/

Over the past decade, machine learning has revolutionised computers’ ability to analyze text through flexible computational models. Beyond text, emerging transformer-based architectures have shown promise as tools to make sense of a range of multi-variate sequences from protein-structures to weather-forecasts due to their structural similarity to written language. Another type of process which has a strong structural similarity to language is human lives. From one perspective, lives are simply sequences of events: We are born, we visit the pediatrician, we start school, we move to a new location, we get married, and so on. Here, we use this similarity to adapt innovations from natural language processing to examine the evolution and predictability of human lives based on day-to-day event sequences.

Spring College on the Physics of Complex Systems

🗓️ 20 Feb to 17 Mar 2023

▶️ indico.ictp.it/event/10059/

Applications deadline: 30/11/2022

The Oxford Summer School in Economic Networks

The dates for the 2023 school are June 26-30.

https://www.maths.ox.ac.uk/events/summer-schools/economic-networks

The Oxford Summer School in Economic Networks seeks to create a stimulating and friendly environment to bring students from varied disciplines together to learn about theories, techniques, quantitative methods, applications and impacts of network theory within economics.

Daniel Harlow presents at the It From Qubit Complexity Workshop at SITP on March 20, 2017.

https://youtu.be/Qbu0i1xO4No

کانال برای اپلای در رشته‌های علوم اعصاب و روانشناسی برای بچه‌های ایران
t.me/applyPN‎