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Transcriptomics Internship Topics

Browse all focused areas across all internship categories under this field.

Transcriptomics Internships with Accommodation

Choose an internship topic, then explore accommodation-enabled internship options at active NTHRYS branch locations in India.

Showing 1–12 of 15 internship topics
Gene Expression Profiling in Tumor Heterogeneity
Interns will analyze RNA-seq datasets to identify differential gene expression patterns across tumor subpopulations and cell types. They will learn techniques for dimensionality reduction, clustering analysis, and interpretation of expression signatures associated with cancer phenotypes and treatment resistance.
AI Cancer Transcriptomics ResearchView internship →
Machine Learning for Cancer Subtype Classification
Interns will develop and train supervised learning models using transcriptomic data to classify cancer subtypes and predict patient outcomes. They will work with algorithms such as random forests, SVMs, and neural networks to build predictive classifiers and evaluate their performance using cross-validation and independent test sets.
AI Cancer Transcriptomics ResearchView internship →
Pathway Analysis and Functional Annotation of Oncogenic Signatures
Interns will conduct enrichment analysis on differentially expressed genes to identify dysregulated biological pathways and functional modules in cancer. They will utilize tools like GSEA, IPA, and Reactome to map gene signatures to cancer-relevant processes such as metastasis, immune evasion, and drug resistance.
AI Cancer Transcriptomics ResearchView internship →
Single-Cell Transcriptomics Data Processing and Analysis
Interns will process and analyze single-cell RNA-seq data to characterize cellular diversity within tumors and the tumor microenvironment. They will perform quality control, normalization, cell type annotation, and trajectory inference to understand cellular hierarchies and cell-cell interactions in cancer contexts.
AI Cancer Transcriptomics ResearchView internship →
Biomarker Discovery and Validation for Cancer Prognosis
Interns will identify candidate prognostic and predictive biomarkers from transcriptomic datasets using statistical analysis and machine learning approaches. They will validate biomarker signatures using independent cohorts and explore their clinical utility for patient stratification and treatment selection in oncology.
AI Cancer Transcriptomics ResearchView internship →
Single-Cell RNA-Seq Analysis of Immune Cell Populations
Interns will process and analyze scRNA-seq datasets to identify and characterize distinct immune cell subsets, including T cells, B cells, and myeloid lineages. They will learn data preprocessing, quality control, clustering, and cell type annotation using bioinformatics tools like Seurat, Scanpy, and CellRanger.
AI Immune Transcriptomics ResearchView internship →
Machine Learning Models for Immune Response Prediction
Interns will develop and train AI/ML models to predict immune responses based on transcriptomic signatures, including classification algorithms for immunotherapy response and survival prediction. This involves feature selection, model validation, and interpretation of feature importance in clinical contexts.
AI Immune Transcriptomics ResearchView internship →
Gene Regulatory Network Analysis in Immune Development
Interns will construct and analyze gene regulatory networks (GRNs) from transcriptomic data to identify key transcription factors controlling immune cell differentiation and activation. They will use tools like SCENIC, motif enrichment analysis, and network visualization to understand immune regulatory mechanisms.
AI Immune Transcriptomics ResearchView internship →
Transcriptomic Biomarker Discovery for Infection and Inflammation
Interns will identify and validate transcriptomic signatures that distinguish immune states during viral, bacterial, or parasitic infections and inflammatory conditions. This includes differential expression analysis, pathway enrichment, and development of potential diagnostic biomarkers.
AI Immune Transcriptomics ResearchView internship →
Deep Learning Applications for Immune Transcriptomics Data Integration
Interns will apply deep learning architectures (autoencoders, VAEs, transformers) to integrate multi-modal immune transcriptomics datasets and learn latent representations of immune states. They will work on cross-sample batch correction, integration of multiple studies, and interpretability of learned representations.
AI Immune Transcriptomics ResearchView internship →
Single-Cell RNA-Seq Analysis of Neural Progenitor Differentiation
Interns will analyze single-cell transcriptomic datasets to identify gene expression patterns during neural progenitor cell (NPC) differentiation into neurons. They will learn clustering techniques, cell trajectory inference, and marker gene identification using tools like Seurat and Monocle to understand developmental stage-specific transcriptional signatures.
Neuronal Differentiation TranscriptomicsView internship →
Temporal Gene Expression Profiling in Neurogenesis
Interns will process bulk and single-cell RNA-seq data from different timepoints of neuronal differentiation to construct gene expression time-series. They will perform differential expression analysis, identify temporal gene clusters, and validate key genes involved in early, intermediate, and late neuronal maturation stages.
Neuronal Differentiation TranscriptomicsView internship →
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