Heidelberg University

Overview

The PhD program in Mathematical Modeling for the Quantitative Biosciences at Heidelberg University equips students with advanced skills in mathematical modeling, simulation, and quantitative analysis of biological systems. This interdisciplinary course fosters collaboration with experimental researchers, allowing students to engage in impactful projects that cover diverse areas, such as gene regulation, signaling pathways, cell mechanics, and population dynamics.

What you'll study

The program integrates rigorous research with a structured training curriculum. Students are encouraged to develop their own research projects while participating in comprehensive teaching, methodological workshops, and interdisciplinary activities.

Core training themes

• Mathematical modeling: Creating models to represent biological processes and systems.
• Computation and simulation: Employing numerical methods, stochastic simulations, and model-based inference techniques.
• Data analysis: Conducting quantitative analyses of experimental data, along with model calibration and validation.
• Multiscale and spatio-temporal methods: Connecting biological mechanisms across different scales and dimensions of space and time.
• Interdisciplinary research practice: Collaborating with experimental teams through internships and hands-on laboratory experiences.

Example PhD project areas

• Investigating the regulation of cell-to-cell variability in prokaryotic organisms.
• Modeling the dual role of immune cells in skin cancer and wound healing interactions.
• Exploring gene expression as a non-equilibrium process.
• Computational modeling of organ size control through Hippo signaling pathways.
• Developing mathematical models for neurogenesis.
• Examining the role of the cytoskeleton during viral infections through modeling.

Internships and research integration

• PhD candidates engage in internships at collaborating experimental labs to acquire quantitative experimental techniques and assist in planning experiments.

Entry requirements

Applicants should possess a master’s degree (or equivalent) with a commendable academic record, indicating a minimum of four years of university-level study. Strong preparation in quantitative disciplines such as mathematics, physics, engineering, computer science, statistics, or related areas is essential, as the program emphasizes the integration of modeling with computational and experimental collaboration.

Career prospects

Graduates of this program are well-prepared for careers in academia, research institutions, and industry sectors that require expertise in mathematical modeling and quantitative analysis of biological systems. The interdisciplinary nature of the training enhances employability in various fields, including biotechnology, pharmaceuticals, healthcare, and environmental science.