PhD position in Probability theory with applications in epidemiology
We seek a PhD student to work with Dr. Pieter Trapman on stochastic models for the spread of infectious diseases. The subject of the PhD project is to develop, extend and rigorously analyse probabilistic models and theory for this spread. This will likely involve branching processes, random graphs, percolation theory and interacting particle systems.
https://www.rug.nl/about-ug/work-with-us/job-opportunities/?details=00347-02S000953P&cat=phd
We seek a PhD student to work with Dr. Pieter Trapman on stochastic models for the spread of infectious diseases. The subject of the PhD project is to develop, extend and rigorously analyse probabilistic models and theory for this spread. This will likely involve branching processes, random graphs, percolation theory and interacting particle systems.
https://www.rug.nl/about-ug/work-with-us/job-opportunities/?details=00347-02S000953P&cat=phd
Job openings in Copenhagen!
3 #PhD/#postdoc positions in the #nerdsitu team at ITUkbh on my COCOONS project on collective coordination.
#ComputationalSocialScience #DataScience #NetworkScience #NLProc
Details here: https://t.co/QpCyuHhYFv
3 #PhD/#postdoc positions in the #nerdsitu team at ITUkbh on my COCOONS project on collective coordination.
#ComputationalSocialScience #DataScience #NetworkScience #NLProc
Details here: https://t.co/QpCyuHhYFv
Lajello
Luca Maria Aiello, PhD - About me
Good news come in pairs. Let me anticipate a complementary project: a book on multilayer network science.
Available from July in the amazing collection.
#multilayer #networks https://t.co/5CEgPAsd8P
Available from July in the amazing collection.
#multilayer #networks https://t.co/5CEgPAsd8P
Come to Copenhagen and do a #PhD on making denoising diffusion probabilistic models (#DDPM) even better!
Job advertisement: https://t.co/t7jJr6Q2SE
Deadline: May 15th
Job advertisement: https://t.co/t7jJr6Q2SE
Deadline: May 15th
DTU Career Site
DTU Career Site Careers
Technology for people
Our Perspectives paper “From diversity to complexity: Microbial networks in soils” is out. Microbial co-occurrence analysis is increasingly used in soil ecology. However, the nature of soil poses challenges to it. https://t.co/gjKpXttRH5
Twitter
Christina Kaiser
Our @SoilBiolBiochem Perspectives paper “From diversity to complexity: Microbial networks in soils” is out: doi.org/10.1016/j.soil…. Microbial co-occurrence analysis is increasingly used in soil ecology. However, the nature of soil poses challenges to it…
We have 6 new 3yr POSTDOC POSITIONs at #HIFMB in Oldenburg, Germany. If you would like to work with me on #Networks and #Foodwebs and join an exciting new institute, this is the perfect opportunity. As a special benefit you could be among the first to work in our new building: https://t.co/eV1jcL9W4J
Twitter
Thilo Gross
We have 6 new 3yr POSTDOC POSITIONs at #HIFMB in Oldenburg, Germany. If you would like to work with me on #Networks and #Foodwebs and join an exciting new institute, this is the perfect opportunity. As a special benefit you could be among the first to work…
"An effective introduction to the Markov Chain Monte Carlo method" (by Wenlong Wang): https://t.co/U9j35gP8qw
"We present an intuitive, conceptual, but semi-rigorous introduction to the celebrated Markov Chain Monte Carlo method using a simple model of population dynamics as our motivation and focusing on a few elementary distributions."
"We present an intuitive, conceptual, but semi-rigorous introduction to the celebrated Markov Chain Monte Carlo method using a simple model of population dynamics as our motivation and focusing on a few elementary distributions."
A gentle introduction to dynamical systems theory
https://fabiandablander.com/r/Dynamical-Systems.html
https://fabiandablander.com/r/Dynamical-Systems.html
Fabian Dablander
A gentle introduction to dynamical systems theory
Dynamical systems theory provides a unifying framework for studying how systems as disparate as the climate and the behaviour of humans change over time. In this blog post, I provide an introduction to some of its core concepts. Since the study of dynamical…
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Infectious diseases and nonlinear differential equations
https://fabiandablander.com/r/Nonlinear-Infection.html
https://fabiandablander.com/r/Nonlinear-Infection.html
Fabian Dablander
Infectious diseases and nonlinear differential equations
Last summer, I wrote about love affairs and linear differential equations. While the topic is cheerful, linear differential equations are severely limited in the types of behaviour they can model. In this blog post, which I spent writing in self-quarantine…
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Forwarded from Complex Networks (SBU)
Interdisciplinary #PhD position in Marseilles, "Emergence of shared conventions in non-human social networks", see https://cpt.univ-mrs.fr/~barrat/phd.html
Deadline for applications: April 29th, 2022
Deadline for applications: April 29th, 2022
Interested applying machine learning, applied mathematics & data science tools for analyzing neural connectivity and sequential activation of neural ensembles associated with sensory computations and learning? We are hiring a #PhD student ! #KISNeuro #NTNU.
https://t.co/nzj2hrFEJX
https://t.co/nzj2hrFEJX
Jobbnorge.no
Ph.D Candidate (224137) | NTNU - Norwegian University of Science and Technology
Job title: Ph.D Candidate (224137), Employer: NTNU - Norwegian University of Science and Technology, Deadline: The application deadline has passed
CNeuro2022: in-person computational neuroscience summer school, Basel, Switzerland - 14 to 21 August 2022. This year's theme: "Learning, Memory, and Decision Making in Brains and Machines." Link for information and application in comment. https://t.co/HZd4kCx1fl
Confererence celebrating the 100th anniversary of Rényi's birth, Budapest, Hungary, 20 June - 23 June 2022
Alfréd Rényi was born on the 20th of March 1921. To celebrate this occasion, the Alfréd Rényi Institute of Mathematics and the Hungarian Academy of Sciences are organising a high-profile conference, representing modern probability, graph theory and networks, information theory, dynamical systems, number theory and other fields in Rényi's spirit. That is: not respecting strictly the borders between these and possibly other areas of pure and applied mathematics.
https://conferences.renyi.hu/renyi100/home
Alfréd Rényi was born on the 20th of March 1921. To celebrate this occasion, the Alfréd Rényi Institute of Mathematics and the Hungarian Academy of Sciences are organising a high-profile conference, representing modern probability, graph theory and networks, information theory, dynamical systems, number theory and other fields in Rényi's spirit. That is: not respecting strictly the borders between these and possibly other areas of pure and applied mathematics.
https://conferences.renyi.hu/renyi100/home
Course: Classical and Quantum Chaos
February 28, 2022 - March 29, 2022
Organizers Meenu Kumari
Chaos, popularly known as the butterfly effect, is a ubiquitous phenomenon that renders a system's evolution unpredictable due to extreme sensitivity to initial conditions. Within the context of classical physics, it often occurs in nonintegrable Hamiltonian systems and is characterized by positive Lyapunov exponents. On the other hand, the notion of nonintegrability and chaos in quantum physics is still not well-understood and is an area of active research. Several signatures have been studied in the literature to identify quantum chaos but all of them fall short in some way or the other. In this course, we will first discuss the notions of classical integrability, and classical chaos and its characterization with Lyapunov exponents. Then, we will discuss a few well-studied signatures of quantum chaos and the subtleties associated with them.
https://pirsa.org/C22018
February 28, 2022 - March 29, 2022
Organizers Meenu Kumari
Chaos, popularly known as the butterfly effect, is a ubiquitous phenomenon that renders a system's evolution unpredictable due to extreme sensitivity to initial conditions. Within the context of classical physics, it often occurs in nonintegrable Hamiltonian systems and is characterized by positive Lyapunov exponents. On the other hand, the notion of nonintegrability and chaos in quantum physics is still not well-understood and is an area of active research. Several signatures have been studied in the literature to identify quantum chaos but all of them fall short in some way or the other. In this course, we will first discuss the notions of classical integrability, and classical chaos and its characterization with Lyapunov exponents. Then, we will discuss a few well-studied signatures of quantum chaos and the subtleties associated with them.
https://pirsa.org/C22018
1 #PhD and 1 #postdoc position are open in my group at University of Genova! If you are interested in fluid mechanics, biological behavior and machine learning please express interest https://t.co/nfpBAfdJFL. Apply by May 15th to receive full consideration!
On the dimensionality of behavior
William Bialek
Significance
How do we characterize animal behavior? Psychophysics started with human behavior in the laboratory, and focused on simple contexts, such as the decision among just a few alternative actions in response to sensory inputs. In contrast, ethology focused on animal behavior in the natural environment, emphasizing that evolution selects potentially complex behaviors that are useful in specific contexts. New experimental methods now make it possible to monitor animal and human behaviors in vastly greater detail. This “physics of behavior” holds the promise of combining the psychophysicist’s quantitative approach with the ethologist’s appreciation of natural context. One question surrounding this growing body of data concerns the dimensionality of behavior. Here I try to give this concept a precise definition.
Abstract
There is a growing effort in the “physics of behavior” that aims at complete quantitative characterization of animal movements under more complex, naturalistic conditions. One reaction to the resulting explosion of high-dimensional data is the search for low-dimensional structure. Here I try to define more clearly what we mean by the dimensionality of behavior, where observable behavior may consist of either continuous trajectories or sequences of discrete states. This discussion also serves to isolate situations in which the dimensionality of behavior is effectively infinite.
https://www.pnas.org/doi/full/10.1073/pnas.2021860119
William Bialek
Significance
How do we characterize animal behavior? Psychophysics started with human behavior in the laboratory, and focused on simple contexts, such as the decision among just a few alternative actions in response to sensory inputs. In contrast, ethology focused on animal behavior in the natural environment, emphasizing that evolution selects potentially complex behaviors that are useful in specific contexts. New experimental methods now make it possible to monitor animal and human behaviors in vastly greater detail. This “physics of behavior” holds the promise of combining the psychophysicist’s quantitative approach with the ethologist’s appreciation of natural context. One question surrounding this growing body of data concerns the dimensionality of behavior. Here I try to give this concept a precise definition.
Abstract
There is a growing effort in the “physics of behavior” that aims at complete quantitative characterization of animal movements under more complex, naturalistic conditions. One reaction to the resulting explosion of high-dimensional data is the search for low-dimensional structure. Here I try to define more clearly what we mean by the dimensionality of behavior, where observable behavior may consist of either continuous trajectories or sequences of discrete states. This discussion also serves to isolate situations in which the dimensionality of behavior is effectively infinite.
https://www.pnas.org/doi/full/10.1073/pnas.2021860119
PNAS
On the dimensionality of behavior | PNAS
There is a growing effort in the “physics of behavior” that aims at complete quantitative
characterization of animal movements under more complex, ...
characterization of animal movements under more complex, ...
#phd Networks and Graphs Collaboratory - Embedding life and health
During your journey through life, you leave behind digital remnants in the health, the social, the educational, the legal system, and many more. This is a rich source of information for finding and visualizing patterns in life trajectories. Technically, we observe a number of streams of events in several channels, and a number of interactions with other individuals. The purpose of this research is to structure these as embeddings in vector spaces making visualizations, clustering, predictive analytics etc possible. Driving questions are in pharmacovigilance, in long covid tracking, in social interactions. Can we develop methods for detecting the impact on life of medications, of having had Covid, and can we quantify or even predict this?
Methodologically, we will develop deep networks inspired by the foundation models in Natural Language Processing like BERT and GPT-3 by graph neural networks and by variational autoencoders. Data-wise the project will rely on already harvested electronic health records from Capital Region and Zealand Region of Denmark comprising 2,4 mio subjects and data from Statistics Denmark on socioeconomic factors from the Danish population.
Supervisor: Mads Nielsen (University of Copenhagen, Computer Science). Co-supervisor: Sune Lehmann Jørgensen(Technical University of Denmark, DTU Informatics).
https://employment.ku.dk/phd/?show=156242
During your journey through life, you leave behind digital remnants in the health, the social, the educational, the legal system, and many more. This is a rich source of information for finding and visualizing patterns in life trajectories. Technically, we observe a number of streams of events in several channels, and a number of interactions with other individuals. The purpose of this research is to structure these as embeddings in vector spaces making visualizations, clustering, predictive analytics etc possible. Driving questions are in pharmacovigilance, in long covid tracking, in social interactions. Can we develop methods for detecting the impact on life of medications, of having had Covid, and can we quantify or even predict this?
Methodologically, we will develop deep networks inspired by the foundation models in Natural Language Processing like BERT and GPT-3 by graph neural networks and by variational autoencoders. Data-wise the project will rely on already harvested electronic health records from Capital Region and Zealand Region of Denmark comprising 2,4 mio subjects and data from Statistics Denmark on socioeconomic factors from the Danish population.
Supervisor: Mads Nielsen (University of Copenhagen, Computer Science). Co-supervisor: Sune Lehmann Jørgensen(Technical University of Denmark, DTU Informatics).
https://employment.ku.dk/phd/?show=156242
employment.ku.dk
PhD-positions
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