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Our client is seeking a Computational Scientist to join the team; this is an opportunity to be involved in the use of state-of-the-art technologies to study the structural genomic variation in human biology, evolution and disease.
The Computational Scientist will work closely with a team of scientists involved in the ongoing studies which includes; accurate identification and characterization of structural variation (SV) in human and other vertebrate genomes, the development of new algorithm/approach for SV detection, and understanding genome evolution in vertebrate species and its impact on genome stability.
In addition, the Computational Scientist will participate in all relevant activities and collaborations as assigned by the PI of the lab to the extent assigned. They include, and are not limited to, helping interns, post-doctoral fellows, scientists and grant writing. Furthermore, they will present results at conferences and workshops.
Responsibilities
Computational Scientist carries out Computational analysis of omics data, algorithm implementation as well as bioinformatics programming and expert reasoning and leadership for the projects. Evaluate data for quality assurance and mining bioinformatics databases. Present the results at the international conferences and workshops and contributes the preparation of manuscripts
Computational Scientist independently establishes collaborations within, provide expert reasoning to the projects relevant to the scientist’s disciplines of research disciplines and lead or co-lead grant writing.
Qualifications
PhD is required with a concentration in Bioinformatics, Computer Science, Biostatistics or other related field
Minimum of five (5) years of experience in Bioinformatics research, excluding time spent to obtain PhD
Experience in High Throughput Sequence (HTS) data analysis (DNA Seq, mRNA Seq, ChIP Seq, micro RNA Seq etc), microarray data analysis, experimental design, data integration, algorithm development, development of sequence analysis tools (bioinformatics programming), evaluation of analytical tools and technology
Experience with “omics” data analysis, statistical genetics & Biostatistics
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