Join the next #JuliaLang#SysBio community call!
🗓️ April 23, 5pm UTC
🔗Link: https://t.co/N9vgBQAA0x
🎤 Speakers:
Aayush Sabharwal: SymbolicIndexingInterface.jl
Augustinas Sukys: Neural approximations of the chemical master eqn
Emily Nieves: Bayesian chemical reaction NNs
We are looking for a talented and enthusiastic candidate for our PhD position to work on the interphase of molecular biology, optical biophysics and AI. Apply here: https://t.co/62MIfW6S3m
PhD student wanted! We are building the first whole-cell dynamic model of yeast! If you are interested in #computationa_biology, modelling, signalling, metabolism, #ageing, method development, and data integration, apply here: https://t.co/PNbWmvqlD7 #phdlife
Not one, not two, not even three, but TWELVE PhD positions at our department! Everything from pure math to AI, bioinformatics and computational biology! All we want for Christmas are your applications! Join us in 2024!
https://t.co/Y7tR66Ttd8
❣️I am looking for a motivated PhD candidate to join me in creating new methods for unraveling cancer evolution @chalmersuniv❣️
Exciting research (if I may say so), great work atmosphere and superb employment benefits❗️
Find out more and spread the word:
https://t.co/bfcOXPuWoH
Join the Julia Systems Biology community calls!
🗓️ December 13, 6 pm UTC
🔗 Call link: https://t.co/qpSUwWUKQE
🎤 Speakers:
- Torkel Loman: Integration of Catalyst.jl with #JuliaLang#sysbio packages.
- @sPersson96 : Parameter estimation with PEtab.jl.
I'm looking for a PhD student to work within an interdisciplinary project related to stress-induced protein aggregation dynamics in living cells. Application deadline 14/03/2022. For more details, visit:
https://t.co/PPy8waHzqY
Please, RT
Seminar on "Scalable Bayesian Inference for Dynamic State-Space Mixed-Effects Models", with Sebastian Persson at 14.00 (Swedish time) on 30 November. Zoom link in the pic below or visit us in room MVL15. @sPersson96
To achieve efficient inference, we slightly perturb the underlying model which speeds up inference by a factor of 35. Furthermore, we benchmarked adaptive MCMC-proposals for the pseudo-marginal steps, and our results showed that the robust AM (RAM) sampler performed best. (5/5)
Want to use modelling to deduce cellular dynamics and sources of cell heterogeneity?
Read our @uPicchini @cannemara @samuel_wiqvist@CvijovicLab preprint about PEPSDI, a mixed-effects inference engine for stochastic dynamic single-cell models. (1/5)
https://t.co/jHH7K7PTvh
More formally, PEPSDI is a Gibbs-sampler performing Bayesian inference for dynamic state-space mixed-effects models. The tractable Gibbs-steps are sampled using HMC, while the intractable steps are sampled using pseudo-marginal approach with (correlated) particle filters. (4/5)