CMU’s PhD in Quantitative Biosciences equips scientists to unite theory and high-dimensional data for breakthroughs in health, ecology, and evolution. Doctoral students model stochastic gene-expression, develop physics-informed neural networks to predict protein folding, and deploy Bayesian epidemiological models during real-time outbreaks. Cross-training in machine learning, micro-fluidics, and policy ensures graduates can both derive equations and deploy solutions in clinics or conservation field sites.
Single-cell multi-omics integration algorithm revealing lineage decisions in hematopoiesis
Stochastic differential-equation model of CRISPR gene-drive dynamics in wild populations
Physics-guided graph neural network predicting conformational ensembles of IDPs
Agent-based model simulating tumor-immune co-evolution under checkpoint therapy
Information-theoretic analysis of neural coding in olfactory circuits
Deep-learning enhancer–promoter interaction predictor using chromatin-contact maps
Optimal experimental-design framework selecting perturbations in synthetic biology
Eco-evolutionary game theory explaining microbial community metabolite exchange
Bayesian change-point detection of zoonotic-spillover events from surveillance data
Nanopore signal deconvolution tool improving detection of base modifications
Biomechanical model linking cell-shape changes to tissue morphogenesis forces
Multi-scale simulation of carbon-fixation pathways in engineered cyanobacteria
Epidemic-forecast ensemble leveraging mobile-device mobility networks
Genome-wide association meta-analysis pipeline for rare disease consortia
Policy brief on genomic data-sharing frameworks balancing privacy and innovation
Citizen-science platform gamifying annotation of 3-D cell-tracking datasets
Quantum-inspired annealing heuristic for protein-design energy landscapes
Synthetic-ecology micro-fluidic chip studying predator–prey oscillations
Explainable AI model of antimicrobial-resistance gene transfer in hospitals
Interactive VR tool teaching principles of stochastic gene networks to undergrads
Decode complex biology with mathematics, computing, and innovation at CMU.
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