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

"The Physics of Algorithms". Discover phase transition in computational problems and their relation to algorithmic hardness

https://youtu.be/ew3xTcEeht0

Several #postdoc positions are available in Budapest in various research groups in discrete mathematics and probability theory.

Information about the research groups are listed here:

https://erdoscenter.renyi.hu/events/drafting-workshop-discrete-mathematics-and-probability-2023

Diffusion Probabilistic Models, Jascha Sohl-Dickstein

https://youtu.be/XCUlnHP1TNM

'Complex systems in Ecology: a guided tour with large Lotka-Volterra models and random matrices" (by Imane Akjouj, Matthieu Barbier, Maxime Clenet, Walid Hachem, Mylène Maïda, François Massol, Jamal Najim, Viet Chi Tran): arxiv.org/abs/2212.06136

"The aim of this review article is to present an overview of the work at the junction of theoretical ecology and large random matrix theory."

At the Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, 21st-24th March 2023 we will be hosting a school concerning stochastic differential equations and the YUIMA R package (Simulation and Inference for SDEs and Other Stochastic Processes, https://cran.r-project.org/web/packages/yuima/index.html

The lectures will be given by members of the YUIMA team. Below is a nearly final program of the school. The dates are fixed. A similar event took place in summer 2019: https://yuimaproject.com/yss2019/

Please feel free to spread information about the school around. If anyone would be interested in coming, they should e-mail me directly. The registration deadline is 28th February 2023. However, we might end registration early if the amount of interested participants exceeds our capacity.

Students who choose to take the school as a course, can obtain 3credits for it. Examination will be through a hand-in assignment. Please let me know if you would be interested in this option.

Tentative program, can be subject to change but dates 21 March-24 March fixed

We assume the participants are familiar with R.
Laboratory means that this session will contain short exercises (ca 15min) to be done by the participants on their own laptops. The YUIMA Conference sessions will be more advanced research oriented talks, everyone is welcome to attend. If you would have a related research topic to and would be interested in presenting, then please let me know. I will see what possibilities we would have.

DAY 1 (Mar 21)
10:45-11:30 Introduction to stochastic calculus I (stochastic processes, Brownian motion, labo with R)
13:00-14:15 Introduction to stochastic calculus II (stochastic integral and SDE, labo with R)
14:25-14:50 What can we do with YUIMA?
15:00-16;00 Linkoping Statistics Seminar + YUIMA Conference

DAY 2 (Mar 22)
09:00-10:15 Simulation of diffusion processes I (Euler-Maruyama approximation and introduction to YUIMA: yuima object, simulation, plot, Black-Scholes model, labo)
10:30-11:30 Simulation of diffusion processes II (simulation of various models for illustrations)
13:00-14:00 Laboratory (simulation with YUIMA)
14:15-15:30 Poisson process and Compound Poisson processes (introduction, simulation, laboratorory)
15:50-16:50 YUIMA Conference

DAY 3 (Mar 23)
09:00-10:15 Inference for diffusion processes I (QMLE)
10:30-11:30 Inference for diffusion processes II (quasi-Bayes estimation)
13:00-14:45 Model selection for diffusion processes
15:00-16:00 YUIMA GUI
16:10-17:10 YUIMA Conference

DAY 4 (Mar 24)
09:00-10:00 Levy processes and Levy driven SDE I (theoretical background and some examples)
10:30-11:30 Levy processes and Levy driven SDE II (simulation in YUIMA)
13:00-16:00 YUIMA Conference

Two #postdoc jobs on infectious disease dynamics, one math modelling, one experimental with Roland Regoes and me at ETH Zurich. More info here: https://jobs.ethz.ch/job/view/JOPG_ethz_cFnH1vVUIrGOtR06hZ and https://jobs.ethz.ch/job/view/JOPG_ethz_7PJ2CQ0RJzQvkVMh1K.