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Bioinformatics Internship Topics

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Bioinformatics Internships with Accommodation

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Showing 37–48 of 105 internship topics
Ortholog and Paralog Identification Using Comparative Genomics
Interns will identify orthologs and paralogs across multiple species using sequence homology searches and synteny analysis. They will characterize gene family evolution, duplication events, and functional divergence patterns using bioinformatic pipelines.
AI Phylogenetics & Comparative Genomics ResearchView internship →
Machine Learning for Species Classification and Evolutionary Inference
Interns will develop and train AI models (neural networks, random forests, SVMs) to classify evolutionary relationships and predict species divergence based on genomic features. They will evaluate model performance using cross-validation and apply interpretability techniques to understand phylogenetic signals.
AI Phylogenetics & Comparative Genomics ResearchView internship →
Genomic Variation Analysis and Population Phylogenetics
Interns will analyze single nucleotide polymorphisms (SNPs), indels, and structural variants across populations using population genetics tools. They will construct population-level phylogenies and study evolutionary processes like selection, migration, and drift.
AI Phylogenetics & Comparative Genomics ResearchView internship →
Functional Annotation Integration in Comparative Genomic Studies
Interns will integrate functional annotations (GO terms, protein domains, pathways) with comparative genomic data to trace functional evolution across species. They will identify conserved functional modules and study how gene function relates to evolutionary divergence patterns.
AI Phylogenetics & Comparative Genomics ResearchView internship →
Deep Learning for Protein Structure Prediction
Interns will work on implementing and optimizing neural network models for predicting 3D protein structures from amino acid sequences. They will utilize frameworks like PyTorch or TensorFlow to train models on datasets such as PDB and evaluate performance metrics against experimental structures.
AI Network Bioinformatics ResearchView internship →
Graph Neural Networks for Drug-Target Interaction
Interns will develop graph-based machine learning models to predict interactions between drug compounds and protein targets. This involves representing molecular structures as graphs and applying GNN architectures to classify binding affinities and interaction patterns.
AI Network Bioinformatics ResearchView internship →
Genomic Data Mining and Variant Effect Prediction
Interns will build computational pipelines to analyze genomic sequencing data and predict the functional impact of genetic variants using machine learning. They will work with tools like GATK and develop models to classify pathogenic versus benign mutations.
AI Network Bioinformatics ResearchView internship →
Sequence Alignment and Homology Modeling
Interns will create optimized algorithms for multiple sequence alignments and develop computational methods for homology-based protein modeling. They will implement or improve sequence comparison tools and validate structural models against experimental data.
AI Network Bioinformatics ResearchView internship →
Network Analysis of Biological Pathways and Gene Regulation
Interns will construct and analyze biological networks from omics data to identify gene regulatory relationships and pathway interactions. They will apply network algorithms and visualization techniques to uncover disease mechanisms and predict biomarkers from complex biological systems.
AI Network Bioinformatics ResearchView internship →
DNA Methylation Pattern Analysis
Interns will develop and apply computational pipelines to analyze DNA methylation patterns across different cell types and disease states using whole-genome bisulfite sequencing (WGBS) data. They will learn to identify differentially methylated regions (DMRs) and correlate methylation changes with gene expression using machine learning classification models.
AI Epigenome Bioinformatics ResearchView internship →
Histone Modification Mapping and Integration
Interns will process and analyze ChIP-seq data to map histone modifications across the genome and integrate multi-omics datasets to understand chromatin architecture. They will work on peak calling algorithms, annotation of regulatory elements, and visualization of epigenetic landscapes across tissues.
AI Epigenome Bioinformatics ResearchView internship →
3D Genome Structure and Chromatin Interaction
Interns will analyze Hi-C and similar chromosome conformation capture data to understand 3D chromatin organization and topologically associating domains (TADs). They will develop tools to correlate chromatin contacts with epigenetic marks and gene regulation patterns using network analysis approaches.
AI Epigenome Bioinformatics ResearchView internship →
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