#phd in Machine learning, inverse problems and signal processing

💰 PhD position “When computational physics meets observations: using machine learning to bridge the gap”

https://academicpositions.fr/ad/labex-lio/2020/phd-position-when-computational-physics-meets-observations-using-machine-learning-to-bridge-the-gap/151934


Objectives
The ultimate goal of the proposed thesis is to build a fast interpolation method on a grid of computational physics simulated images (in a broad sense as it can also be 3D volumes or spectra). With such a method, we will quickly have an approximation of a simulated image from any possible set of parameters, without having to run the expensive simulation. It then will be possible to use any method (optimization, Bayesian inference) to derive the so sought-after distribution of parameters.

The main idea is to use a deep learning framework to build the interpolator. Indeed, all possible modeled images are concentrated on a lower-dimensional subspace or manifold. Deep neural networks such as Generative Adversarial Networks (GAN) appear to be very efficient to model manifolds and could be much more efficient interpolators than classical polynomial interpolators. Trained on a grid on simulated images, these deep neural networks will produce continuous approximations of the images. As a toy example, in a properly defined manifold, the images of a single circle vary continuously with the circle radius. Interpolation between two images of circles with different radius must follow this manifold whereas any polynomial interpolation will produce an image with two circles rather than an image of a single circle with intermediate radius.

Grids of models are quite ubiquitous in physics, and hence such a project can have important impact. To ensure that it will be both robust and useful in practice, the deep learning based interpolator will be developed for two different applications: (i) planet forming disk characterization using VLTI in collaboration with J. Kluska (KU Leuven) and (ii) reconstruction of mantle structure based on geophysical surface observations


Application deadline
May the 1st, 2021

💰 I'm hiring 2 graduate students for my ERC project, focused on predicting depression onset in 2,000 students. Looking for a #PhD position? Come work with me @UniLeiden!

Core topics: #depression, #complexity, #timeseries, #networks, #EMA, & #MachineLearning.

You can find the 2 positions in the link below.
https://t.co/jGQIPX2DuG

Here is a blog in which I describe the project in some more detail, including a short video. /
https://t.co/pdZx1k7xXW

💰 Come do a #PhD with me in #cognitive #data #science and #complex #networks at @UniofExeter!

In an EPSRC scholarship by @exetercompsci , we'll investigate how to give structure to #knowledge and its influence in socio-cognitive systems.

Deadline 25/01/21:
https://t.co/BBokMFV3za

📯We are hiring!📯
4-year fully funded #PhD position in Sample-Efficient Probabilistic Machine Learning @UnivHelsinkiCS with links to @FCAI_fi

Please see blurb below, and full ad here: https://t.co/w7Y3Nhxn3w

Application deadline: Jan 10, 2021. Please RT! https://t.co/Nr5plMuJ2C

💰 The Helsinki Doctoral Education Network in Information and Communications Technology (HICT) has 40 open positions for Doctoral Students!

The participating units of HICT have currently funding available for exceptionally qualified doctoral students. We offer the possibility to join world-class research groups, with multiple research projects to choose from. The activities of HICT and the open positions are structured along five research area specific tracks:

🔵 Algorithms and machine learning
🔵 Life science informatics
🔵 Networks, networked systems and services
🔵 Software and service engineering and systems
🔵 User centered and creative technologies

If you wish to be considered as a potential new doctoral student in HICT you can apply to one or a number of doctoral student positions. We welcome applicants with diverse backgrounds, and qualified female candidates are explicitly encouraged to apply.

#phd Deadline 02.02.2021

http://www.hict.fi/spring2021

‘A divisive disease” .
https://t.co/4Z7x9sRaaL

#Networks2021 session on advances in #multilayer #network analysis. Abstract submission open until Jan 24

https://networks2021.net/program

Apply now for the Spring College on the Physics of Complex Systems: https://t.co/eoXGuCUTdT

#ComplexSystems

An ICTP Virtual Meeting:

Starts 22 Feb 2021
Ends 19 Mar 2021

http://indico.ictp.it/event/9442/

📺 The medical test paradox: Can redesigning Bayes rule help?

https://t.co/DMpt1sKIAe

We're looking for #undergraduate students interested in #research in #mathBio and #quantBio this summer -- come join us (virtually) at the NSF-Simons Center for @QuantBiology! Application deadline: Jan. 25, 2021

https://www.quantitativebiology.northwestern.edu/opportunities/undergraduate-summer-research/

📺 https://www.aparat.com/v/qcMGt

🗺 جهان کوچک ما:
https://rastaiha.ir/article/13

💰 International Max Planck Research School IMPRS “From Molecules to Organisms”
International Max Planck Research School IMPRS “From Molecules to Organisms”

#PhD positions in the life sciences https://academicpositions.com/ad/international-max-planck-research-school-imprs-molecules-organisms/2020/phd-positions-in-the-life-sciences-starting-september-2021/151797

💰 #PhD Position in Signal Processing for Music Analysis - Academic Positions

https://en.academicpositions.be/ad/ku-leuven/2020/phd-position-in-signal-processing-for-music-analysis/152296

Generating Multivariate Gaussian Random Numbers

https://aishack.in/tutorials/generating-multivariate-gaussian-random/

کنفرانس فیزیک اقتصاد و اقتصاد پیچیدگی

⭕️ ۱۳ و ۱۴ اسفندماه ۱۳۹۹
دانشگاه شهید بهشتی و انجمن مالی ایران (مجازی)

مهلت ارسال مقالات: ۹ بهمن‌ماه ۱۳۹۹

ثبت‌نام و اطلاعات بیشتر:
http://complexityeconomics.ir


@EconophysicsConf
______________________________
#کنفرانس #سیستم_های_پیچیده
@sbu_physics

Apply now for the 20th International Workshop on #ComputationalPhysics and #MaterialsScience: Total Energy and Force Methods.

📌 Deadline for applications with Talks and/or Posters: 15 January 2021

📌 Deadline for other applications: 7 February 2021

https://t.co/Q1VQhLui8M

Complexity meets criticality

⏰ شنبه ۱۳ دی ماه؛ ساعت ۱۶

🔴 لینک ورود به GoogleMeet

🔸 لطفا هنگام ورود دوربین و میکروفون خود را خاموش نمایید.
_________________________
#سمینار_هفتگی
@sbu_physics

Lifetime of links influences the evolution towards structural balance

S.Arabzadeh, M.Sherafati, F.Atyabi, G.R.Jafari, K.Kułakowski

https://www.sciencedirect.com/science/article/abs/pii/S0378437120309870?dgcid=coauthor

Abstract
A fully connected network is investigated with signed (friendly or hostile) links. We consider a time evolution which drives the network to a structurally balanced state. Usually a hypothesis is made tacitly that the states of links can be permanent. However in real networks these states can fluctuate. In this paper, we assign a lifetime to each link. When a link age exceeds its lifetime, its sign is substituted by a random value
and its age is set to zero. Then, two asymptotic behaviors are observed. When the lifetime is large, the system is balanced with only small fluctuations. When the lifetime is short, the system fluctuates randomly, far from the balanced state. A crossover is observed between these two regimes. The age distribution of the links depends on the lifetime. The results are discussed in the context of data on selected conflicts between political actors in Europe and the Middle East in the XX century.