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Immunoinformatics Internship Topics

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Immunoinformatics Internships with Accommodation

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Showing 1–10 of 10 internship topics
Machine Learning Model Development for B-Cell Epitope Prediction
Interns will develop and optimize machine learning models using Python libraries (scikit-learn, TensorFlow) to predict B-cell epitopes from amino acid sequences. They will work with datasets like IEDB and BepiPred to train classifiers and evaluate performance metrics including sensitivity, specificity, and AUC-ROC curves.
AI Epitope Prediction & Mapping ResearchView internship →
Deep Learning Architecture Implementation for T-Cell MHC Binding Prediction
Interns will implement and fine-tune deep neural networks (CNN, RNN, Transformer-based models) to predict MHC-peptide binding affinities for T-cell epitope discovery. They will utilize existing frameworks like NetMHCpan and MHCflurry while exploring novel architectural improvements on benchmark datasets.
AI Epitope Prediction & Mapping ResearchView internship →
Immunogenicity Assessment and Epitope Validation Pipeline Development
Interns will design computational pipelines to assess epitope immunogenicity by integrating multiple prediction tools and scoring functions. They will create automated workflows for cross-validation against experimental immunogenicity data and develop visualization tools for epitope mapping results.
AI Epitope Prediction & Mapping ResearchView internship →
Multi-Pathogen Epitope Database Curation and Integration
Interns will curate and integrate epitope data from multiple sources (IEDB, UniProt, PubMed) for various pathogenic organisms using bioinformatics tools and databases. They will perform quality control, standardization, and annotation of epitope sequences while building searchable indexed databases for downstream analysis.
AI Epitope Prediction & Mapping ResearchView internship →
Structure-Based Epitope Prediction Using Protein Modeling and Molecular Docking
Interns will utilize protein structure prediction tools (AlphaFold, Modeller) and molecular docking software to identify conformational epitopes from 3D protein structures. They will analyze antibody-antigen interactions, surface accessibility, and geometric properties to refine epitope predictions beyond sequence-based methods.
AI Epitope Prediction & Mapping ResearchView internship →
Machine Learning Model Development for MHC-Peptide Binding Prediction
Interns will develop and optimize machine learning models to predict MHC-peptide binding affinities using datasets like IEDB and NetMHCpan. This involves feature engineering, model training, validation, and benchmarking against existing prediction tools to improve immunogenicity assessment accuracy.
AI Immunogenicity Prediction ResearchView internship →
Deep Learning Architectures for T-Cell Epitope Identification
Interns will implement and compare deep learning models (CNNs, RNNs, transformers) to identify and classify T-cell epitopes from protein sequences. The focus will be on training robust neural networks that can generalize across different HLA alleles and pathogenic organisms.
AI Immunogenicity Prediction ResearchView internship →
Immunogenicity Risk Assessment Pipeline Development
Interns will design and implement an integrated bioinformatics pipeline that combines multiple prediction algorithms to assess immunogenicity risks for therapeutic proteins and vaccines. This includes data preprocessing, algorithm integration, and visualization of immunogenic hotspots.
AI Immunogenicity Prediction ResearchView internship →
B-Cell Epitope Prediction Using Sequence and Structural Analysis
Interns will develop computational methods combining sequence-based and 3D structure-based approaches to predict linear and conformational B-cell epitopes. Work will involve machine learning model optimization and validation against experimental antibody binding data.
AI Immunogenicity Prediction ResearchView internship →
Cross-Reactive Epitope Discovery and Immunological Impact Assessment
Interns will use AI models to identify cross-reactive epitopes between pathogenic antigens and human proteins, assessing potential autoimmune risks. This involves sequence homology analysis, structural modeling, and statistical evaluation of immunological cross-reactivity implications.
AI Immunogenicity Prediction ResearchView internship →
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