Bovine TB (BTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production, particularly in many developing countries. My current work involves exploring the species specific and cross species response pathways to Mycobacterium by intergrating multiple 'omics level data. Additionally, I have developed a package to integrate the results of omic analysis with Genome Wide Association Data to uncover causal variation. Skills and experience ;• 7 years of experience manipulating and analysing large data sets and building statistical models. • Extensive experience visualising big data for articles and conference presentations. • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, gradient boosting, etc.) and their real-world advantages/drawbacks.• Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modelling, clustering, decision trees, neural networks, etc.• Strong problem-solving skills with an emphasis on data analysis, interpretation and hypothesis generation.• Experience using statistical computer languages (R and Python) to manipulate data and draw insights from large data sets.• System administration on two Debian high-end servers, as well as deep working experience in a Linux environment. • Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.• Writing and publishing papers in scientific journals.

Project 1 : Comparative genomic identification and validation of β-defensin genes in the Ovis aries genome (https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-017-3666-x)Project 2 : Predicting the effect single nucleotide polymorphims would have on transcription factors upstream from beta defensins, and relating those effects to bovine fertility.