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Bioinformatics Internship Topics

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Bioinformatics Internships with Accommodation

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Showing 1–12 of 105 internship topics
Machine Learning for Genomic Variant Classification
Interns will develop and train machine learning models to classify genomic variants as pathogenic or benign using large-scale sequencing datasets. They will work with feature engineering from genetic data, model optimization, and validation against clinical databases to improve variant interpretation accuracy.
AI Genome-Scale Data Analysis ResearchView internship →
Metagenomic Assembly and Taxonomic Profiling
Interns will process next-generation sequencing data from complex microbial communities to perform genome assembly and species identification. They will implement or optimize bioinformatic pipelines for taxonomic classification and analyze microbial diversity patterns across different environmental or clinical samples.
AI Genome-Scale Data Analysis ResearchView internship →
Deep Learning for Protein Structure Prediction from Genomic Data
Interns will apply deep learning algorithms to predict protein structures and functions from genomic sequences, exploring architectures like transformers and graph neural networks. They will validate predictions against experimental protein structures and analyze the relationship between genetic variations and structural changes.
AI Genome-Scale Data Analysis ResearchView internship →
Comparative Genomics and Evolutionary Analysis at Scale
Interns will conduct large-scale comparative genomic analyses across multiple species or strains to identify conserved regions, synteny patterns, and evolutionary relationships. They will implement computational methods for multiple sequence alignment, phylogenetic reconstruction, and functional annotation of conserved elements.
AI Genome-Scale Data Analysis ResearchView internship →
Gene Expression Pattern Mining from Multi-Omics Data
Interns will integrate and analyze multi-omics datasets (genomics, transcriptomics, proteomics) using statistical and machine learning methods to discover gene expression patterns and regulatory relationships. They will develop visualization tools and predictive models to link genetic variants with expression phenotypes and disease states.
AI Genome-Scale Data Analysis ResearchView internship →
Deep Learning Models for Protein Sequence Alignment
Interns will develop and optimize neural network architectures (transformers, LSTMs, or attention mechanisms) for improving protein sequence alignment accuracy. They will work with datasets like SCOP and Pfam to train models that can predict optimal alignment parameters and compare performance against classical algorithms like BLAST and Smith-Waterman.
Machine Learning Sequence Alignment ResearchView internship →
Protein Structure Prediction and Drug Binding Analysis
Interns will develop and optimize machine learning models for predicting 3D protein structures and analyzing drug-protein interactions. They will work with datasets like AlphaFold outputs and molecular docking simulations to identify potential therapeutic compounds and validate binding predictions using computational methods.
Machine Learning for Biotech Innovation ResearchView internship →
Genomic Sequence Classification and Variant Detection
Interns will build deep learning pipelines to classify genomic sequences, identify disease-causing mutations, and detect structural variants in DNA/RNA data. This involves training neural networks on large-scale genomic datasets and implementing quality control measures for clinical applicability.
Machine Learning for Biotech Innovation ResearchView internship →
Genomic Variant Calling Using Machine Learning
Interns will implement ML-based approaches to identify and classify genetic variants from DNA sequencing data by leveraging sequence alignment outputs. This work includes building classification models to distinguish true variants from sequencing artifacts and optimizing alignment-to-variant pipelines for clinical accuracy.
Machine Learning Sequence Alignment ResearchView internship →
Medical Image Analysis for Disease Diagnosis
Interns will develop convolutional neural networks and computer vision algorithms to analyze biomedical imaging data including MRI, CT scans, and microscopy images for disease detection and segmentation. They will focus on improving diagnostic accuracy and interpretability of model predictions in clinical contexts.
Machine Learning for Biotech Innovation ResearchView internship →
Multiple Sequence Alignment Optimization with Graph Neural Networks
Interns will explore graph neural network architectures to model relationships between multiple sequences and improve alignment quality for large sequence families. They will focus on reducing computational complexity while maintaining alignment accuracy for evolutionary and functional annotation studies.
Machine Learning Sequence Alignment ResearchView internship →
Biomarker Discovery Through Multi-Omics Data Integration
Interns will apply machine learning techniques to integrate and analyze multi-omics datasets (genomics, proteomics, metabolomics) to identify disease biomarkers and therapeutic targets. This includes dimensionality reduction, feature selection, and predictive modeling for precision medicine applications.
Machine Learning for Biotech Innovation ResearchView internship →
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