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URDME 1.2 Released

Posted on December 18, 2012December 18, 2012 By admin No Comments on URDME 1.2 Released

We are happy to announce the release of URDME 1.2. New features include support for Comsol 4.1, 4.2 and 4.3. We have also added the ability to compile the solvers independently of Matlab libraries. This will greatly simplify deployment of URDME jobs by StochSS down the line.

To get URDME 1.2: www.urdme.org

Or visit us on GitHub.

Reaction Diffusion Master Equation, URDME

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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 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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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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