Bioinformatics Post-Doc - Cancer Diagnostics
Paramount has a fantastic opportunity for Bioinformatics/Comp Bio/Data Science/Statistical Genetics Post-Docs and PhD grads, to be working in the R&D team of one of the UK's top cancer diagnostics biotechs! This is a rare opportunity to be working right at the front of cutting edge developments in cancer diagnostics, and be involved with all stages from concept, clinical validation to commercial product.
What the job involves:
- Statistical analysis and interpretation of NGS data
- Contribute to the development of novel algorithms for the analysis of cancer genomic data
- Support in building bioinformatics analysis pipelines
- Work in partnership with the software team to produce clean, production ready code
Ideal skills required are:
- PhD in the field of Bioinformatics, Computational Biology, Statistical Genetics, Data Science or a related discipline that combines computational analysis and genomics
- Experience analysing NGS data
- Strong programming skills in R or Python
- Ideally some experience of building novel algorithms, computational models, data mining of machine learning
- Experience/Knowledge of cancer genomics would be fantastic (but not absolutely necessary)
- Brilliant communication skills, lots of enthusiasm and a passion for new technologies in science!
This is an excellent opportunity for any Bioinformatician/Data Scientist/Computational Biologist who is looking to pursue a career in the genomics industry. On offer is a competitive salary, fantastic team environment, exciting projects and the chance to develop your skills across a broad range of disciplines from machine learning to software development to genomic analysis! To be considered for this opportunity, please send your CV to Emilie Francis at email@example.com or if you'd like more information, call Emilie on 07943443704.
Bioinformatics, Bioinformatician, Diagnostics, Oncology, Cancer, Data Science, Machine Learning, PHD, Post-Docs, Cambridge, Algorithm, Statistical Genetics, NGS, Genomics, Computational Biology, R&D