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Metagenomics Internship Topics

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Metagenomics Internships with Accommodation

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Showing 1–12 of 15 internship topics
Deep Learning Models for Metagenomic Sequence Assembly
Interns will develop and optimize neural network architectures (CNNs, RNNs, Transformers) for predicting overlaps between metagenomic reads and improving contig assembly quality. They will work with real sequencing datasets to train models that outperform traditional graph-based assembly algorithms in handling complex microbial communities.
Machine Learning Metagenome Assembly ResearchView internship →
Graph Neural Networks for Metagenomic De Bruijn Graph Construction
Interns will implement and refine graph neural network approaches to represent and traverse de Bruijn graphs constructed from metagenomic data. The focus will be on reducing memory footprint and computational complexity while maintaining assembly accuracy in high-diversity environmental samples.
Machine Learning Metagenome Assembly ResearchView internship →
Binning and Classification of Assembled Contigs Using Machine Learning
Interns will develop machine learning pipelines to classify metagenomic contigs into taxonomic bins and identify novel organisms using features like tetranucleotide frequency, coverage depth, and marker genes. They will compare supervised and unsupervised approaches on benchmark datasets and real environmental samples.
Machine Learning Metagenome Assembly ResearchView internship →
Error Correction in Long-Read Metagenomic Sequences Using Hybrid ML Approaches
Interns will create machine learning models to identify and correct sequencing errors in long-read technologies (PacBio, Oxford Nanopore) used in metagenomic studies. They will integrate short-read alignments and deep learning to improve consensus accuracy for downstream assembly and annotation tasks.
Machine Learning Metagenome Assembly ResearchView internship →
Automated Quality Assessment and Parameter Optimization for Assembly Pipelines
Interns will develop machine learning systems that predict optimal assembly parameters and evaluate assembly quality metrics (N50, completeness, contamination) without requiring reference genomes. They will create automated decision-making tools that select the best assembly strategy for diverse metagenomic datasets based on sequencing characteristics.
Machine Learning Metagenome Assembly ResearchView internship →
Microbial Community Assembly and Succession in Soil
Interns will analyze metagenomic sequencing data to identify microbial taxa, track community composition changes over time, and investigate factors driving microbial succession in soil ecosystems. They will use bioinformatics pipelines to process raw sequencing reads and perform comparative analysis across different soil environments.
AI Soil Metagenomics ResearchView internship →
Functional Gene Annotation and Metabolic Pathway Prediction
Interns will annotate genes from soil metagenomic assemblies, predict metabolic pathways involved in nutrient cycling (carbon, nitrogen, phosphorus), and assess functional diversity within microbial communities. This involves using databases like KEGG and CAZy to link genetic information to ecosystem functions.
AI Soil Metagenomics ResearchView internship →
Machine Learning Models for Soil Microbiome Classification
Interns will develop and train machine learning algorithms to classify soil samples based on metagenomic profiles and predict soil properties or microbial functions. They will work with taxonomic and functional data to build predictive models for soil health indicators.
AI Soil Metagenomics ResearchView internship →
Antibiotic Resistance Gene Detection and Characterization in Soil
Interns will analyze metagenomic data to identify and characterize antibiotic resistance genes (ARGs) in soil microbiomes, assess their prevalence across different soil types, and investigate resistance mechanisms. This includes mapping ARGs to pathogenic organisms and evaluating environmental risk factors.
AI Soil Metagenomics ResearchView internship →
Soil-Plant Microbiome Interactions and Host-Associated Microbial Networks
Interns will integrate soil metagenomic data with plant rhizosphere studies to understand microbial-plant interactions and construct co-occurrence networks. They will identify keystone species and explore how plant health outcomes correlate with specific microbial community structures and compositions.
AI Soil Metagenomics ResearchView internship →
16S rRNA Gene Amplicon Sequencing and Bacterial Community Analysis
Interns will learn to design and execute 16S rRNA amplicon sequencing workflows to characterize bacterial and archaeal communities in plant root and rhizosphere samples. They will perform quality control, sequence processing using QIIME2 or similar platforms, and conduct taxonomic classification and alpha/beta diversity analyses to understand microbial composition across different plant genotypes and environmental conditions.
Plant Microbiome Metagenomics StudyView internship →
Shotgun Metagenomic Assembly and Functional Gene Annotation
Interns will work with whole-community DNA sequencing data to perform de novo metagenomic assembly and binning of microbial genomes from plant microbiome samples. They will annotate functional genes, identify metabolic pathways related to plant nutrient acquisition and defense, and use tools like KEGG, CAZy, and antiSMASH to characterize plant-beneficial microbial functions.
Plant Microbiome Metagenomics StudyView internship →
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