Postdoc positions in Bayesian Modelling and Inference in Computational Systems Biology at University of Manchester
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magnus
Jun 22, 2017, 10:48:53 AM6/22/17
to Machine Learning News
Applicants are invited for two Research Associate positions in computational statistics/machine learning for modelling gene expression dynamics at the University of Manchester. The posts are associated with a Wellcome Trust Senior Investigator Award “Global dynamics of mRNA accumulation and translation in embryonic development” which aims to understand how genetic information is decoded to specify particular cell fates during development.
One post involves the development and application of novel computational methods for the analysis of data from single-cell assays, including single-molecule fluorescent in situ hybridization (smFISH, see e.g. [1]) and live cell imaging technologies (see e.g. [2]) targeting transcription and translation at single-cell resolution.
The other post involves developing and applying novel algorithms for probabilistic modelling and Bayesian inference over time course data from high-throughput sequencing assays including RNA-Seq, Ribo-Seq and nascent transcription assays, building on Gaussian process methods for modelling transcription dynamics developed by us [3].
References:
[1] N.E. Phillips, C.S. Manning, T. Pettini, V. Biga, E. Marinopoulou, P. Stanley, J. Boyd, J. Bagnall, P. Paszek, D.G. Spiller M.R.H. White, M. Goodfellow, T. Galla, M. Rattray, N. Papalopulu (2016). “Stochasticity in the miR-9/Hes1 oscillatory network can account for clonal heterogeneity in the timing of differentiation” eLife, 5, e16118.
[2] Phillips, N. E., Manning, C., Papalopulu, N., & Rattray, M. (2017). Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes. PLOS Computational Biology, 13(5), e1005479.
[3] Honkela, A., Peltonen, J., Topa, H., Charapitsa, I., Matarese, F., Grote, K., Stunnenberg, H. G., Reid, G., Lawrence, N. D., & Rattray, M. Genome-wide modelling of transcription factor kinetics reveals patterns of RNA processing delays. Proc Natl Acad Sci 112, 13115 (2015).
Both positions will require close collaboration with experimental collaborators generating these datasets, helping both in the experimental design and data analysis.
See http://goo.gl/Nz3jlV and http://goo.gl/mWcKmQ for further details and application procedures.
One post involves the development and application of novel computational methods for the analysis of data from single-cell assays, including single-molecule fluorescent in situ hybridization (smFISH, see e.g. [1]) and live cell imaging technologies (see e.g. [2]) targeting transcription and translation at single-cell resolution.
The other post involves developing and applying novel algorithms for probabilistic modelling and Bayesian inference over time course data from high-throughput sequencing assays including RNA-Seq, Ribo-Seq and nascent transcription assays, building on Gaussian process methods for modelling transcription dynamics developed by us [3].
References:
[1] N.E. Phillips, C.S. Manning, T. Pettini, V. Biga, E. Marinopoulou, P. Stanley, J. Boyd, J. Bagnall, P. Paszek, D.G. Spiller M.R.H. White, M. Goodfellow, T. Galla, M. Rattray, N. Papalopulu (2016). “Stochasticity in the miR-9/Hes1 oscillatory network can account for clonal heterogeneity in the timing of differentiation” eLife, 5, e16118.
[2] Phillips, N. E., Manning, C., Papalopulu, N., & Rattray, M. (2017). Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes. PLOS Computational Biology, 13(5), e1005479.
[3] Honkela, A., Peltonen, J., Topa, H., Charapitsa, I., Matarese, F., Grote, K., Stunnenberg, H. G., Reid, G., Lawrence, N. D., & Rattray, M. Genome-wide modelling of transcription factor kinetics reveals patterns of RNA processing delays. Proc Natl Acad Sci 112, 13115 (2015).
Both positions will require close collaboration with experimental collaborators generating these datasets, helping both in the experimental design and data analysis.
See http://goo.gl/Nz3jlV and http://goo.gl/mWcKmQ for further details and application procedures.
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