Postdoctoral Scholar
Current● Data management and analysis: Collecting, processing, and analyzing biological data, including genomic, transcriptomic, proteomic, and metabolomic data, using various software and programming languages.● Algorithm development: Developing and implementing machine learning algorithms, statistical models, and other computational methods to analyze biological data and solve research problems.● Software development: Designing and developing software tools and pipelines for processing and analyzing biological data.● Data visualization: Creating visualizations to communicate research findings and biological data in a clear and intuitive manner.● Collaboration: Working closely with biologists, geneticists, and other experts to identify research questions, develop experimental designs, and analyze and interpret results.● Scientific writing: Writing manuscripts, grant proposals, and other scientific documents to communicate research findings and secure funding.● Keeping up with the latest developments in the field: Staying up-to-date with the latest research in bioinformatics, computational biology, machine learning, and AI, and applying this knowledge to inform your work.● Presenting at conferences and meetings: Presenting research findings and new methodologies at scientific conferences and seminars.● Maintaining ethical standards: Ensuring that all research is conducted ethically and complies with relevant regulations and guidelines.● Statistical and machine learning molding for large-scale human genetic data, translation research for HIV/AIDS, cardiovascular cancer disease, and their interaction. NGS- RNA-seq, scRNA-Seq, WES, WGS, discovery-based computational modeling, regression analysis, and predictive modeling.●CRISPR cas9 editing● Tools: R, Python, Tensorflow, Keras, ANN, CNN, RNN, HTStream, FastQC, BWA, Bowtie2, STAR, GATK, SAM, Tools, dbSNP, BED Tools, Limma-Voom, Bioconductor.
