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Biostatistics Internship Topics

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Biostatistics Internships with Accommodation

Choose an internship topic, then explore accommodation-enabled internship options at active NTHRYS branch locations in India.

Showing 1–12 of 25 internship topics
Cox Proportional Hazards Model Optimization
Interns will develop and optimize machine learning algorithms to improve Cox proportional hazards model performance for censored data analysis. Work includes implementing regularization techniques, feature selection methods, and comparing traditional statistical approaches with modern ML alternatives for survival prediction.
Machine Learning for Survival Analysis ResearchView internship →
Deep Learning for Right-Censored Data
Interns will explore neural network architectures specifically designed for survival analysis, including DeepHit and other deep learning frameworks that handle censoring mechanisms. Projects involve building models that capture complex non-linear relationships in time-to-event data and evaluating performance using concordance indices.
Machine Learning for Survival Analysis ResearchView internship →
Competing Risks Analysis Using Machine Learning
Interns will develop ML models to handle competing risks scenarios where multiple terminal events can occur, implementing methods like Fine-Gray models and machine learning alternatives. Focus areas include model validation, risk stratification, and clinical interpretation of competing risk predictions.
Machine Learning for Survival Analysis ResearchView internship →
Feature Engineering for Survival Prediction
Interns will design and implement feature engineering pipelines specifically tailored for survival analysis, including time-varying covariates, interaction terms, and dimensionality reduction techniques. Work includes evaluating feature importance through survival-specific metrics and optimizing predictive performance on high-dimensional datasets.
Machine Learning for Survival Analysis ResearchView internship →
Benchmark Development for Survival ML Models
Interns will create comprehensive benchmarking frameworks comparing classical statistical methods with machine learning approaches across multiple survival datasets. Tasks include standardizing evaluation metrics, developing cross-validation strategies appropriate for censored data, and publishing comparative performance analyses.
Machine Learning for Survival Analysis ResearchView internship →
Bayesian Hierarchical Models for Clinical Trial Data
Interns will develop and implement hierarchical Bayesian models for analyzing multi-center clinical trials, focusing on borrowing strength across sites and populations. They will work with real trial datasets to estimate treatment effects while accounting for between-site variability using MCMC and variational inference techniques.
AI Bayesian Biostatistical Methods ResearchView internship →
Prior Specification and Sensitivity Analysis in Biomedical Studies
Interns will investigate methods for eliciting, constructing, and validating prior distributions in biostatistical applications, including expert elicitation approaches and historical data integration. They will conduct comprehensive sensitivity analyses to understand how prior choices impact posterior inference in genomics and epidemiological studies.
AI Bayesian Biostatistical Methods ResearchView internship →
Adaptive Bayesian Designs for Personalized Medicine Trials
Interns will design and simulate adaptive clinical trial frameworks using Bayesian methods for precision medicine applications, incorporating patient subgroup identification and real-time treatment allocation. They will implement response-adaptive randomization strategies and evaluate their operating characteristics through computational studies.
AI Bayesian Biostatistical Methods ResearchView internship →
Bayesian Survival Analysis and Competing Risks Modeling
Interns will develop Bayesian methods for time-to-event analyses, including survival models with competing risks and frailty components for heterogeneous patient populations. They will apply these methods to oncology and cardiovascular datasets while incorporating censoring mechanisms and complex covariate structures.
AI Bayesian Biostatistical Methods ResearchView internship →
Machine Learning Integration with Bayesian Biostatistical Inference
Interns will explore hybrid approaches combining machine learning algorithms with Bayesian statistical frameworks for high-dimensional biomedical data analysis, including genomics and medical imaging applications. They will develop methods for uncertainty quantification in predictive models and implement scalable inference algorithms for large datasets.
AI Bayesian Biostatistical Methods ResearchView internship →
Predictive Modeling for Drug Response in Precision Medicine
Interns will develop and validate machine learning models to predict patient-specific drug responses using genomic and clinical data. This involves feature engineering, model selection, cross-validation, and performance evaluation of algorithms like random forests and gradient boosting in precision medicine contexts.
AI Precision Medicine Biostatistics ResearchView internship →
Statistical Analysis of Genomic Data in Clinical Trials
Interns will perform exploratory data analysis and statistical testing on high-dimensional genomic datasets from precision medicine clinical trials. They will work with variant calling, quality control, and population stratification analyses to identify biomarkers associated with treatment outcomes.
AI Precision Medicine Biostatistics ResearchView internship →
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