Job Purpose

• To provide high quality research support and undertake internationally competitive research aimed at publication thereby contributing to the academic and research mission of the University.

Key Roles & Responsibilities

Position Summary

The Emirati Genome Project (EGP) is national project that aims to identify key genetic profile that can explain the unique part of the Emirati Genome. The aim is to use Artificial Intelligence (A.L.) or Machine Learning (M.L.) applications on processed Emirati genome data obtained from Next Generation Sequencing to identify unique patterns that will then be characterized through mathematical modelling and genome bioinformatics approaches. We are looking for someone to assist in the day-to-day testing and running of code fragments that are written for EGP analysis as well as troubleshooting any issues that may arise when running the code on BIG data.

The ideal candidate should have MSc or PhD degree in computer science or biomedical science or similar with proven work experience in the areas of A.I. or M.L. applied to BIG data.

He/She should have proven experience in analysing and deriving meaningful interpretation from BIG data, basic understanding of the statistical analysis that underlie the A.I./M.L. analysis, understanding of basic mathematics, statistics, intermediate/advanced coding experience in R or Python preferably in Linux/Unix platform and experience in debugging and tracing code to troubleshoot software issues. In addition, the candidate should have basic understanding of the genome including gene structure, SNPs, non-coding and coding regions, regulatory elements and Genome Wide Association Studies.

Experience in coding for genomics analysis and knowledge of existing bioinformatics pipelines to analyse genomic data is desirable but not essential, as full training will be provided.

Major Responsibilities

• Assist in the day-to-day testing of Python or R code to help enhance the speed and accuracy of analysis of the Emirati Genome Project
• Troubleshoot issues with the in-house software by debugging or tracing the code and identifying any semantic errors
• Implement basic code in any programming language to visualize the output from the A.I. or M.L. software related to genome analysis
• Functional analysis of customized advanced bioinformatics code to shorten the time it takes to analyse Emirati Genome data
• Interact with other members of the Emirati Genome Project analytics team to pass the in-house developed software to them to use in the analysis of the EGP data

Competencies

• Extensive experience in BIG data analysis using A.I. and M.L. approaches including understanding the limitation of A.I./M.L. in BIG data analytics
• Intermediate/Advanced coding experience in either Python or R using various libraries such as NumPy, TensorFlow, SciPy, Keras and Pandas
• Experience in software debugging through tracing and error checking approaches
• Basic understanding of statistics and mathematical principles that underlines A.I. algorithms
• Basic understanding of genetics and the data structures that underlie genetics such as chromosome, gene, DNA, SNPs, mutation, coding and non-coding regions, and promoter regions
• Ability to work as part of a team but also use own initiative to meet tight deadlines

Qualifications

Qualifications & Experience

Required Qualifications

Master degree or PhD in Computer Science and information system or any related Science major, or similar with proven work experience in the areas of A.I. or M.L. applied to BIG data.

Required Experience

All applicants must demonstrate a proven track record of, or high potential for, high-quality scientific research, as evidenced by their publication records and/or letters of recommendation. Candidates interested in translational research and entrepreneurship are encouraged to apply.

Should you require further assistance or if you face any issue with the online application, please feel to contact the Recruitment Team ( ).

Primary Location: Khalifa University - Abu Dhabi, UAE
Job: Research Associate
Schedule: Regular
Shift: Standard
Job Type: Full-time

Times Higher Education