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BACKGROUND
A great opportunity is now available in Oxford for a Data Scientist to use state-of-the-art Machine Learning and Deep Learning methods, working with complex genetic data. This place-holder will design and implement novel approaches to learning from large scale data using state-of-art machine learning techniques. The position offers the opportunity to work with leading scientists and a talented technical team, using an integrated data resource that is unrivalled in the field, in order to deliver cutting-edge science in genomic analysis. There are open positions available to individuals with suitable experience in data science companies, as well as individuals who have recently completed their PhDs, existing postdoctoral fellows, or existing PIs.REQUIREMENTS
You should have:- A strong quantitative background in statistics, machine learning or data science
- Proven experience of analysing large-scale data sets in a scientific context using state-of-art machine learning methods
- The ability to summarize and visualize complex datasets to look for patterns or potential biases through detailed analysis
- The ability to discuss and explain complex ideas to both specialists and non-specialists
- The ability to pick up complex concepts by interacting with specialists outside your own field
- Experience in applying standard statistical approaches, and in contributing to the development of novel computational tools
PREFERENCES
Ideally, you additionally have:- Experience in genetics or genomics
- Experience in the medical sciences
- Experience with scientific programming languages, Python, and C/C++
COMPENSATION
There are some great benefits available with this role including a competitive salary, pension, healthcare and the opportunity to work with some of the most influential individuals in the industry.HOW TO APPLY
Please do not hesitate to contact Harvey Uppal at huppal[at]pararecruit.com or call (+44) 121 616 3407 to discuss this opportunity further.
Keywords: Data, Scientist, Genomics, Bioinformatics, Human, Genetics, Machine Learning, Deep Learning, Theano, TensorFlow, Random Forest, Statistics, Analysis, Python, C++, Oxford.
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