Postdoctoral Associate
Qualifications: PhD Computational Biophysics
Skills: Expert in MD simulations (AMBER, NAMD, and GROMACS), molecular docking (AutoDock Vina), and protein structure prediction (RosettaFold and AlphaFold). Proficient in next-generation sequencing (NGS) data processing using tools such as STAR, BWA, SAMtools, and Bowtie, along with workflow automation using NextFlow, nf-core, and Snakemake. Experienced in gene expression analysis with HTSeq, DESeq2, and edgeR, as well as genomic data manipulation with BEDTools. Skilled in data visualization (IGV, UCSC Genome Browser). Analyzing bulk RNA sequencing, single-cell RNA sequencing, ATAC-seq, proteomics, and DNA methylation data. Expertise in variant calling (Mutect2, Strelka, Control-FREEC, SnpEff and so on). Strong knowledge of high-performance computing (HPC) environments and cloud platforms for large-scale data analysis. Proficient in containerization technologies (Docker and Singularity) for reproducible workflows. Extensive experience in applying machine learning techniques, including regression models, random forests, and clustering (t-SNE, PCA, K-means), as well as deep learning frameworks like Keras and PyTorch. Programming skills include R, Python, Bash, Perl, and SQL, with a strong emphasis on data cleaning, wrangling, and visualization.
”With a robust background in computational biophysics, I am committed to advancing the understanding of disease mechanisms, particularly in oncology, to identify novel therapeutic targets. By harnessing advanced bioinformatics pipelines, state-of-the-art computational tools, machine learning, and deep learning techniques, I aim to elucidate complex protein interactions, integrate multi-omics datasets, and spearhead innovative drug discovery efforts to improve patient outcomes.”
Fun Fact
When I’m not diving into datasets, I’m buried in books—exploring topics like AI, economics, history, and domestic and international politics. By day, I analyze data; by night, I devour debates!
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