Princeton’s Ph.D. in Quantitative and Computational Biology (QCB) equips students to tackle the complexity of biological systems through quantitative frameworks, bioinformatics, and computational tools. Below are curated research topics that span the intersections of data science, biology, and advanced modeling.
Network Inference of Gene Regulatory Circuits in Developmental Systems
Single-Cell RNA-Seq Data Analysis for Cell Lineage Mapping
Machine Learning Approaches for Protein-Protein Interaction Prediction
Stochastic Modeling of Intracellular Reaction Networks
Quantitative Modeling of Immune Response Dynamics
Spatiotemporal Analysis of Cell Migration in Cancer Metastasis
Bioinformatic Pipelines for Structural Variant Detection in Genomes
Evolutionary Dynamics Simulations of Microbial Populations
Multi-Omics Integration for Personalized Medicine Applications
High-Throughput CRISPR Screening Data Interpretation
Comparative Genomics for Phylogenetic Reconstruction
Probabilistic Graphical Models in Gene Expression Analysis
Synthetic Biology Circuits: Predictive Design and Simulation
Mathematical Models of Circadian Rhythms in Mammals
Computational Neuroscience of Sensory Encoding Mechanisms
Simulation of Biochemical Pathways Using Kinetic Monte Carlo Methods
Deep Learning for Histopathological Image Classification
Bayesian Inference in Epidemiological Modeling
Population Genetics and Coalescent Theory with Big Data
Quantitative Trait Loci Mapping in Complex Traits
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