- The candidate will work on data generated from secondary/tertiary analysis pipelines and use AI/ML, probabilistic programming, and other statistical genomics approaches to analyze various large-scale omics data sets to better understand disease etiology, identify novel drug targets, and discover biomarkers for use in precision medicine.
Role & Responsibilities
* data generated from secondary/tertiary analysis pipelines and use AI/ML
* probabilistic programming
* statistical genomics approaches to analyze various large-scale omics data sets to better understand disease etiology
* identify novel drug targets
* discover biomarkers for use in precision medicine.
Skills & Qualifications
* M.Sc. or Ph.D. in Engineering, Computational Statistics, Computer Science, Biostatistics, Bioinformatics, or related field with a minimum of 2-years of related industry and/or academic experience
* Experience in machine learning, deep learning, statistical methodology, predictive modeling and algorithm development
* Minimum of 2 years of industry and/or academic research laboratory management experience
* Familiar with NGS data analysis, using common bioinformatics tools (BWA, STAR, Picard, GATK etc.), and knowledge of publicly available genomics databases (i.e. ENCODE, GEO, TCGA, CCLA)
* Advanced programming skills with fluency in at least Python and/or R, with extensive experience using modern machine learning and deep learning libraries (TensorFlow, PyTorch, Edward, sklearn, caret, etc.)
* Proven ability to design and code production grade machine AI/ML applications, along with a strong ability to visualize 'big data'
* Ability to work on high-performance computing system and manage cloud computing environments (e.g. AWS) with experience working with GPUs
* Strong communication and presentation skills with the ability to translate and communicate results to individuals of diverse backgrounds