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StochSS 1.6 is now officially released!

Posted on July 6, 2015November 6, 2015 By admin No Comments on StochSS 1.6 is now officially released!

New capabilities of Version 1.6 include: Spatial visualization now supports animation, wireframe rendering, and mesh slicing FlexCloud: run ‘cloud’ jobs on dedicated hardware (in addition to using EC2) Import SBML models Many bug fixes and stability enhancements Details and instructions on how to obtain the code can be found on the Download page Tutorials are…

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News, PyURDME, Software, Stochastic Chemical Kinetics, StochSS

Open PhD position

Posted on February 5, 2015February 5, 2015 By admin No Comments on Open PhD position

We are currently hiring a PhD student to work on the project From Single Cells to Tumors – Multiscale Simulation of Stochastic (multi)cellular Systems . Deadline for application: March 15. Instructions for how to apply can be found here: Instructions for application.

Multiscale methods, News, PyURDME, Reaction Diffusion Master Equation

StochSS 1.4: Introducing Spatial Stochastic Modeling and Simulations

Posted on September 1, 2014September 1, 2014 By admin No Comments on StochSS 1.4: Introducing Spatial Stochastic Modeling and Simulations

We are excited to announce the release of StochSS 1.4 Version 1.4 includes spatial stochastic simulation capabilities powered by  PyURDME (http://www.pyurdme.org/). Details and instructions on how to obtain the code can be found on the Download page Tutorials are available on the Documentation page Linda Petzold and Chandra Krintz – University of California Santa Barbara Per Lotstedt and Andreas Hellander – Uppsala…

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News, PyURDME, Reaction Diffusion Master Equation, Software, Stochastic Chemical Kinetics, StochSS

PyURDME 1.0

Posted on June 25, 2014June 25, 2014 By admin No Comments on PyURDME 1.0

After more than a year of development, we are happy to announce the release of PyURDME 1.0.0! PyURDME is a Python module for spatial stochastic simulation model development and simulation. PyURDME is connected to URDME in that is uses a modified version of its core solver. While URDME is mainly designed as an interactive Matlab…

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News, PyURDME, Reaction Diffusion Master Equation, Software, StochSS, URDME

Data-and simulation-driven life science. Much of our work in eScience and applied ML has applications in life science, and in Systems Biology in particular. We aim to enable data-and simulation-driven scientific discovery.

HASTE - a cloud native framework for intelligent processing of image streams: http://haste.research.it.uu.se/

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Andreas HellanderFollow

Andreas Hellander
A_HellanderAndreas Hellander@A_Hellander·
11 May

Are you using StochSS? Please help us gather insights into what is working well and what can be improved by filling in this short survey https://forms.gle/mEqfASuUd3MDWuPS9

@LindaPetzold @briandrawert @mhucka

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A_HellanderAndreas Hellander@A_Hellander·
9 May

Apply to this PhD student position in the eSSENCE and SciLifeLab graduate school in data-intensive science!

This project is the intersection of cybersecurity and big data with main supervisor @sztoor.
https://www.uu.se/en/about-uu/join-us/details/?positionId=501061

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A_HellanderAndreas Hellander@A_Hellander·
28 Apr

PhD position in the eSSENCE/@scilifelab graduate school in data-intensive science with @cnettel: https://uu.se/en/about-uu/join-us/details/?positionId=501716 Apply and be part of a new interdiciplinary research effort @UU_University!

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A_HellanderAndreas Hellander@A_Hellander·
23 Apr

If you are a current user of StochSS please let us know your thoughts by filling out this brief user survey: https://forms.gle/3r836iph8gqFEpZX7

#systemsbiology #stochss @LindaPetzold @briandrawert @prashant_rsingh

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Retweet on TwitterAndreas Hellander Retweeted
AssistSweASSIST Sweden@AssistSwe·
14 Apr

Soon the partners in the ASSIST project will attend a workshop on federated learning arranged by Scaleout. Partners from different countries (Sweden, Belgium, Netherlands, Turkey) will contribute with nodes that train a segmentation network.

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Decentralized AI, Federated Learning. One focus area of the group is development of methods and software to address decentralized and privacy-preserving AI. We are core contributors to the FEDn open source framework for scalable federated machine learning:

https://github.com/scaleoutsystems/fedn
Introduction to Federated Learning by Andreas Hellander
Join the discussion on Decentralized AI:

Scaleout Systems is a spin-out from ISCL on a mission to enable decentralized AI and federated learning to production.

https://www.scaleoutsystems.com/

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