💰 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
https://t.co/UdzqAuJMs7
www.math.ku.dk
PhD stipends in Statistics and the mathematics of Insurance and Economics
A number of attractive PhD stipends in statistics and the mathematics of insurance and economics are available at the department. Application deadline: Nov 15, 2020.
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
Click here to find out more and apply online 👉 https://t.co/wLuULx9Vog
#science #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
#complex_systems #physics #math
Click here to find out more and apply online 👉 https://t.co/wLuULx9Vog
#complex_systems #physics #math
💰 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
📢 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
Twitter
SEES Lab
👩🏽🎓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! 🙏🏽RT will…
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
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
YouTube
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…
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…
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
☄️ High Energy, Cosmology
and Astroparticle Physics
♾️ Mathematics
⚛️ Condensed Matter and
Statistical Physics
You can find more details here: https://t.co/HZdyO3aNgN
#postdoc
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
https://fcai.fi/news/2020/10/13/large-selection-of-ai-related-courses-open-up-new-possibilities-for-students-from-diverse-disciplines
FCAI
Large selection of AI-related courses open up new possibilities for students from diverse disciplines — FCAI
Host universities of FCAI offer a wide range of courses related to artificial intelligence and machine learning.
💰 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.
https://t.co/opgLeEHsOE
Start date: 1 October 2021!
Your PhD should be dated between 31 May 2016 and 31 May 2021.
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
https://t.co/2WN7UZYMuU
ocw.mit.edu
Machine Learning for Healthcare | Electrical Engineering and Computer Science | MIT OpenCourseWare
This course introduces students to machine learning in healthcare, including the nature of clinical data and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving…
💰 The deadline to apply for the PhD positions in KTH is approaching! #KTH #datascience #machinelearning #AI #PhD #PhDposition
https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:355118/type:job/where:4/apply:1
https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:355118/type:job/where:4/apply:1
💰 We are currently looking for passionate Data Engineers and Data Scientists for our HQ in Berlin, who can improve food-delivery experience for millions of our customers, present in more than 40 countries!
You will develop innovative systems to automate marketing campaigns, design a tailored user experience for each customer and question the existing decision-making processes with ML and advanced analytics solutions.If you're a creative problem solver who is eager to deliver solutions and hungry for a new adventure, an international workplace is waiting for you in the heart of Berlin!
Senior data engineer (Python):
https://lnkd.in/dvzFPZU
Senior data scientist:
https://lnkd.in/dMaCQgC
#dataengineering #datascience #machinelearning #ml #data #deliveryhero
Quick peek at what the team does:
https://lnkd.in/dP2cuNf
You will develop innovative systems to automate marketing campaigns, design a tailored user experience for each customer and question the existing decision-making processes with ML and advanced analytics solutions.If you're a creative problem solver who is eager to deliver solutions and hungry for a new adventure, an international workplace is waiting for you in the heart of Berlin!
Senior data engineer (Python):
https://lnkd.in/dvzFPZU
Senior data scientist:
https://lnkd.in/dMaCQgC
#dataengineering #datascience #machinelearning #ml #data #deliveryhero
Quick peek at what the team does:
https://lnkd.in/dP2cuNf
lnkd.in
LinkedIn
This link will take you to a page that’s not on LinkedIn
Simplicial Complexes and Dynamics
Ginestra Bianconi
Monday, November 2, 2020, 2:00 pm – 3:00 pm
Online Event: https://events.ceu.edu/2020-11-02/simplicial-complexes-and-dynamics
ABSTRACT
Higher order networks which allow to go beyond the framework of pairwise interactions are attracting increasing attention. In fact, in a large variety of complex systems including the brain, collaborations networks or face-to-face social interactions it is important to capture many-body interactions between two or more nodes. Simplicial complexes are the topological objects that can encode these high-order interactions as they are not only formed by nodes and links as networks but include also higher-order simplices such as triangles, tetrahedra and so on. In this talk I show how the network geometry and topology of simplicilal complexes determines higher-order dynamics. I will present models of simplicial complexes and discuss their emergent topology and geometry. These models will be used to reveal the properties of higher-order topological synchronization and diffusion dynamics. These results will allow us to unveil the surprising effect of network topology and geometry have on dynamics.
Ginestra Bianconi
Monday, November 2, 2020, 2:00 pm – 3:00 pm
Online Event: https://events.ceu.edu/2020-11-02/simplicial-complexes-and-dynamics
ABSTRACT
Higher order networks which allow to go beyond the framework of pairwise interactions are attracting increasing attention. In fact, in a large variety of complex systems including the brain, collaborations networks or face-to-face social interactions it is important to capture many-body interactions between two or more nodes. Simplicial complexes are the topological objects that can encode these high-order interactions as they are not only formed by nodes and links as networks but include also higher-order simplices such as triangles, tetrahedra and so on. In this talk I show how the network geometry and topology of simplicilal complexes determines higher-order dynamics. I will present models of simplicial complexes and discuss their emergent topology and geometry. These models will be used to reveal the properties of higher-order topological synchronization and diffusion dynamics. These results will allow us to unveil the surprising effect of network topology and geometry have on dynamics.
Social distancing in pedestrian dynamics and its effect on disease spreading
Sina Sajjadi, Alireza Hashemi, Fakhteh Ghanbarnejad
Download PDF
Non-pharmaceutical measures such as social distancing, can play an important role to control an epidemic in the absence of vaccinations. In this paper, we study the impact of social distancing on epidemics for which it is executable. We use a mathematical model combining human mobility and disease spreading. For the mobility dynamics, we design an agent based model consisting of pedestrian dynamics with a novel type of force to resemble social distancing in crowded sites. For the spreading dynamics, we consider the compartmental SIE dynamics plus an indirect transmission with the footprints of the infectious pedestrians being the contagion factor. We show that the increase in the intensity of social distancing has a significant effect on the exposure risk. By classifying the population into social distancing abiders and non-abiders, we conclude that the practice of social distancing, even by a minority of potentially infectious agents, results in a drastic change on the population exposure risk, but reduces the effectiveness of the protocols when practiced by the rest of the population. Furthermore, we observe that for contagions which the indirect transmission is more significant, the effectiveness of social distancing would be reduced. This study can provide a quantitative guideline for policy-making on exposure risk reduction.
Sina Sajjadi, Alireza Hashemi, Fakhteh Ghanbarnejad
Download PDF
Non-pharmaceutical measures such as social distancing, can play an important role to control an epidemic in the absence of vaccinations. In this paper, we study the impact of social distancing on epidemics for which it is executable. We use a mathematical model combining human mobility and disease spreading. For the mobility dynamics, we design an agent based model consisting of pedestrian dynamics with a novel type of force to resemble social distancing in crowded sites. For the spreading dynamics, we consider the compartmental SIE dynamics plus an indirect transmission with the footprints of the infectious pedestrians being the contagion factor. We show that the increase in the intensity of social distancing has a significant effect on the exposure risk. By classifying the population into social distancing abiders and non-abiders, we conclude that the practice of social distancing, even by a minority of potentially infectious agents, results in a drastic change on the population exposure risk, but reduces the effectiveness of the protocols when practiced by the rest of the population. Furthermore, we observe that for contagions which the indirect transmission is more significant, the effectiveness of social distancing would be reduced. This study can provide a quantitative guideline for policy-making on exposure risk reduction.
🦠 تحقیقی نشان میدهد بیش از ۸۰ درصد بیماران COVID-19 کمبود ویتامین D دارند:
https://t.co/yAtKhT30M3
https://t.co/yAtKhT30M3
www.endocrine.org
Study finds over 80 percent of COVID-19 patients have vitamin D deficiency
Over 80 percent of 200 COVID-19 patients in a hospital in Spain have vitamin D deficiency, according to a new study published in the Endocrine Society’s Journal of Clinical Endocrinology & Metabolism.
Complex Systems Studies
Social distancing in pedestrian dynamics and its effect on disease spreading Sina Sajjadi, Alireza Hashemi, Fakhteh Ghanbarnejad Download PDF Non-pharmaceutical measures such as social distancing, can play an important role to control an epidemic in…
🎉 SUT students, Sina Sajjadi and Alireza Hashemi, shine in 2020 international conference
SUT students won the second place in the contest for the best 30-second movie illustrating the dynamics of biological systems for their decent study titled “Social distancing in pedestrian dynamics and its effect on disease spreading”.
SUT students won the second place in the contest for the best 30-second movie illustrating the dynamics of biological systems for their decent study titled “Social distancing in pedestrian dynamics and its effect on disease spreading”.
Forecasting elections using dynamical systems (with compartmental models of infection): https://t.co/t2IXPBiXSG
2020 election forecasts: https://t.co/UQ9MXqAJLh
2020 election forecasts: https://t.co/UQ9MXqAJLh
“nonlocality confers tremendous potential on topological materials: If a property is not defined locally, then it cannot be destabilized by local defects... The topological age thus promises a class of materials with unusually robust properties.” https://t.co/Ydg8S0XgoG
Physics Today
Dawn of the topological age?
Nontrivial electron band structures may enable a new generation of functional materials.
The science of super-spreading. Cool animations and super clear explanations.
https://t.co/E9zZgPPi11
https://t.co/E9zZgPPi11
vis.sciencemag.org
Gyms. Bars. The White House. See how superspreading events are driving the pandemic
Preventing hot spots of COVID-19 transmission has emerged as a key challenge in the fight against the virus