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Bioinformatics Internship Topics

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Bioinformatics Internships with Accommodation

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Showing 25–36 of 105 internship topics
Cryo-EM Data Processing and Structure Refinement
Interns will process cryo-electron microscopy data and participate in structural reconstruction pipelines using tools like RELION or Cryosparc. They will learn image processing, particle classification, and density map refinement to contribute to determining protein structures from experimental cryo-EM data.
AI Protein Structure Bioinformatics ResearchView internship →
RNA-Seq Data Processing and Quality Control
Interns will learn to preprocess raw RNA-sequencing data, perform quality assessment using tools like FastQC and MultiQC, and implement alignment pipelines using STAR or Bowtie2. They will develop skills in handling large-scale genomic datasets and creating reproducible bioinformatics workflows.
AI Transcriptome Bioinformatics ResearchView internship →
Differential Gene Expression Analysis
Interns will analyze gene expression changes between biological conditions using statistical methods and tools such as DESeq2, edgeR, or Limma. They will interpret results through biological context, perform validation studies, and create publication-quality visualizations of expression patterns.
AI Transcriptome Bioinformatics ResearchView internship →
Machine Learning for Transcriptome Classification
Interns will develop and train machine learning models to classify samples based on transcriptomic profiles using techniques like random forests, neural networks, or support vector machines. They will focus on feature selection, model validation, and interpreting model predictions in the context of disease or phenotype classification.
AI Transcriptome Bioinformatics ResearchView internship →
Pathway and Functional Enrichment Analysis
Interns will perform systems-level analysis of transcriptomic data using pathway databases (KEGG, Reactome, GO) and enrichment tools like GSEA and Metascape. They will integrate multiple omics data types to identify key biological processes and construct gene regulatory networks.
AI Transcriptome Bioinformatics ResearchView internship →
Single-Cell RNA-Seq (scRNA-seq) Data Analysis
Interns will process and analyze single-cell transcriptomic datasets using specialized tools such as Seurat, Scanpy, or Cell Ranger. They will perform cell type identification, trajectory inference, and RNA velocity analysis to understand cellular heterogeneity and developmental processes.
AI Transcriptome Bioinformatics ResearchView internship →
Metagenomic Read Assembly and Binning Optimization
Interns will work on developing and optimizing algorithms for assembling metagenomic sequencing reads and performing genome binning to separate microbial species from complex environmental samples. This involves benchmarking existing assembly tools, implementing quality assessment metrics, and exploring machine learning approaches to improve binning accuracy across diverse microbial communities.
AI Metagenomics Pipeline Development ResearchView internship →
Taxonomic Classification and Abundance Profiling
Interns will develop computational pipelines for accurate taxonomic assignment of metagenomic sequences using reference databases and machine learning classifiers. They will implement abundance profiling methods to quantify microbial community composition and create visualization tools for interpreting taxonomic diversity across samples.
AI Metagenomics Pipeline Development ResearchView internship →
Functional Annotation and Pathway Analysis
Interns will build pipelines to predict and annotate gene functions from assembled metagenomic contigs and assess metabolic pathways present in microbial communities. This includes integrating databases like KEGG and creating tools to link functional profiles with environmental conditions and sample metadata.
AI Metagenomics Pipeline Development ResearchView internship →
Quality Control and Data Preprocessing Workflows
Interns will develop robust preprocessing pipelines for handling raw metagenomic sequencing data, including quality filtering, adapter removal, contamination detection, and normalization. They will establish comprehensive QC metrics and create automated workflows to ensure data integrity throughout the analysis pipeline.
AI Metagenomics Pipeline Development ResearchView internship →
Machine Learning for Microbiome Pattern Recognition
Interns will apply supervised and unsupervised machine learning techniques to identify patterns, predict microbial community composition, and classify samples based on metagenomic signatures. This includes feature selection, model validation, and developing interpretable models for understanding microbiome-phenotype associations.
AI Metagenomics Pipeline Development ResearchView internship →
Phylogenetic Tree Construction and Validation
Interns will learn to construct phylogenetic trees using multiple sequence alignment tools and methods (UPGMA, neighbor-joining, maximum likelihood). They will validate tree topologies through bootstrapping analysis and comparative assessment across different datasets and organisms.
AI Phylogenetics & Comparative Genomics ResearchView internship →
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