(1) Open Position :: Postdoc in image analysis and cancer genomics
The computational biology lab of Dr. Florian Markowetz offers a position for a postdoctoral researcher interested in image analysis and cancer genomics.
Project: We are interested in the interplay between genomic features of tumours and the microenvironment, as it is visible in histopathological images. Using resources already available at the CRI we plan to combine copy-number, SNP and mRNA expression data with quantitative assessment of H&E stained image slides of tumour morphology and more specific stainings, e.g. CD4 and CD8, in tissue microarrays in subsets of samples. We will pursue several specific sub-projects: (1) We will develop integrated histopathological-genomic predictors of survival, taking into account the tissue architecture of the tumour as well as genomic markers; (2) We will deconvolute molecular profiles by cellular heterogeneity and identifying cell-type specific signatures from mixed profiles; (3) We will analyze the interactions and co-locations of cell types in the tumour microenvironment, e.g. how are immune cells located relative to cancer cells.
The ideal applicant combines a strong background in image analysis and machine learning methods. Experience with genomic data analysis (e.g. ChIP seq analysis and motif finding) and statistical modelling (using R or Matlab) as well as experience in medical research are desirable.
If you are highly motivated to work in an interdisciplinary and very collaborative environment at an internationally recognized research institute, apply at the official CRUK job webpage (POS00107).
References
- Beck et al, Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med. 2011 Nov 9;3(108):108ra113.
(2) Open Position :: Postdoc in dynamic modeling
The computational biology lab of Dr. Florian Markowetz offers a position for a postdoctoral researcher interested in dynamic modeling in cancer systems biology.
Project: The goal of this project is to combine a dynamic systems biology model of Estrogen Receptor (ER) signaling with a genome-wide map of epigenetic and genetic determinants of ER binding in cell lines and primary breast cancers. In collaboration with Jason Carroll’s lab at CRI, which is world-leading in ER biology, we will develop a computationally driven experimental approach in which a computational model of ER signalling and binding guides experiments to more fully understand ER dynamics. The final model will contain (i) ligand inputs like estrogen and growth factors, (ii) upstream signalling pathways that regulate ER and co-factor activity (e.g. EGF and IGF pathways), and (iii) binding behaviour of ER and co-factors as output. We will then combine this quantitative model of ER dynamics with epigenetic and genetics determinants of ER binding by measuring SNPs and methylation in cell lines and primary breast tumours.
The ideal applicant has a strong background in dynamic modeling of complex systems, eg with differential equations. Experience with genomic data analysis (e.g. ChIP seq analysis and motif finding) and statistical modelling (using R or Matlab) as well as experience in medical research are a further advantage.
If you are highly motivated to work in an interdisciplinary and very collaborative environment at an internationally recognized research institute, apply at the official CRUK job webpage (POS00108).
References:
- Tyson et al. (2011) Dynamic modelling of oestrogen signalling and cell fate in breast cancer cells. Nature Rev Cancer 2011 2.
- Ross-Innes et al (2012) Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature 481, 389-393. 3.
- Zwart W et al. (2010) Estrogen receptor-positive breast cancer: a multidisciplinary challenge. Wiley Int Rev: Sys Bio and Med 2010
(3) Open Position :: Postdoc in systems genetics
The genetics lab of Prof. Sir Bruce Ponder and the computational biology lab of Dr. Florian Markowetz at the Cancer Research UK Cambridge Research Institute offer a joint position for a postdoctoral researcher interested in statistical and computational approaches to systems genetics in cancer.
The Ponder laboratory focuses on genetic susceptibility to breast and other common cancers [1] and the Markowetz laboratory on integrative systemic analyses [2].
Projects: The function of the two transcription factors FoxA1 and estrogen receptor (ER) are key drivers of breast cancer and standard breast cancer therapy is based on inhibition of ER function. In collaboration with the laboratory of Jason Carroll at CRI, we now wish to explore to which extent genetic variation in the population affects ER/FoxA1 binding and function in order to better understand its role in susceptibility to disease as well as response to therapy. Existing ChIP-Seq [3] data sets will be mined to explore genome-wide allele-specific binding.
We are also developing a project in lung cancer, based on the hypothesis that individual differences in airway epithelium response to smoke injury, measured by gene expression, may predict risk. If this proves to be correct, we will mine the risk signatures for network and pathway information that may identify target for preventative intervention.
The position bridges an experimental and a computational lab and is ideal if you are interested in data analysis and method development motivated by close collaborations with experimentalists.
The ideal applicant has a strong background in genomic data analysis (e.g. ChIP-seq analysis and motif finding) and statistical modelling (using R or Matlab) as well as data visualization and exploration (e.g. with Affy's Integrated Genome Browser). Experience in medical research is desirable.
If you are highly motivated to work in an interdisciplinary and very collaborative environment at an internationally recognized research institute, apply at the official CRUK job webpage (POS00106).
References
- DF Easton, ..., BAJ Ponder Nature 2007. PMID 17529967
- Yuan, Savage, Markowetz, PLoS Comp Bio 2011 PMID 22028636
- CS Ross-Innes, ..., JS Carroll, Nature 2012 PMID 22217937
