A data-science-driven internship evaluating raw transcriptomic and single-cell datasets to map complex disease expressions and locate drug targeting leads.

• Ph.D. candidates or advanced M.Sc/M.Tech students focusing on Biostatistics, Data Science, or Computational Biology.
• Proficiency in R or Python (specifically pandas, numpy, and bioconductor matrices).

• Analyze multi-omic datasets using differential expression frameworks (such as DESeq2 or edgeR).
• Perform high-density dimensionality reductions like UMAP or t-SNE for clustering analysis.
• Visualize biological pathways and gene network systems to formulate functional hypotheses.