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Computational Biologist - Machine Learning

Leinster   •  Negotiable  •  Permanent, Full time

Computational Biologist - Machine Learning

Due to rapid expansion, a young, innovative Biotech based in vibrant Dublin are seeking a talented Computational Biologist with a strong background in machine learning or artificial intelligence to join their thriving Bioinformatics team.
This innovative genomics company have developed a unique platform, and are now harnessing cutting edge machine learning techniques to analyse big data and discover insights of biological significance.

The ideal Computational Biologist will have:

-MSc or PhD in a related field (Bioinformatics, Computer Science, Mathematics)

-Experience performing data analysis using biological, multi-omics data
(genomics, NGS, proteomics, metabolomics, epigenomics etc.)

-Strong mathematical and statistical mind, with a background in algorithm development, artificial intelligence, machine learning or deep learning techniques

-Mature programming skills with a variety of languages - particular strength in python preferred

-Strong statistical background, proficient with R

On offer is a competitive package, central location, access to cutting edge technologies, diverse set of projects, and an exciting, fast paced environment to grow your skills. If you'd like to learn more about how to apply for this fantastic opportunity, send across a copy of your CV to efrancis@pararecruit.com. For an informal chat, call Emilie on 0121 616 3477.

Key words: Computational Biologist, Bioinformatics, Machine Learning, Artificial Intelligence, Dublin, Ireland, Data Science, Genomics, NGS, Proteomics, Big Data, Deep Learning,

Paramount Recruitment Limited provides services as an agency and an employment business. We regularly have similar roles in this area. Please see our website for details or send your CV in to us to find out the latest opportunities.


You MUST be eligible to work in Leinster. Learn more

  • Agency: Paramount Recruitment
  • Posted: 11th October 2017
  • Expires: 8th November 2017
  • Reference: EFDUBML
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