PhD in Bayesian Transport Modelling for Nanoparticles

Delft University of Technology via AcademicTransfer; Netherlands; On-site; EUR 36,000 - 45,000; Full time

Job summary

Delft University of Technology (TU Delft) invites applications for a PhD candidate to build a computational model central to a Horizon Europe project on nanoparticle transport in human tissue. You will join the SYNERGIE consortium and lead in silico work within TU Delft, collaborating with international partners.

The role involves Bayesian inference, Python programming, and developing predictive models of particle transport, with a start date in January 2027.

Qualifications

• MSc in a quantitative field is required.
• Demonstrated experience building and solving ODE or dynamical systems models.
• Demonstrated experience with Bayesian inference in a probabilistic programming framework such as PyMC, Stan or NumPyro.
• Strong scientific programming in Python. Please point us to a repository.
• Excellent written and spoken English.

Responsibilities

• Build a mechanistic model of how particles move between nine human tissues, and calibrate it against experimental data using Bayesian inference.
• Work out which physical and chemical properties of a particle decide where it ends up.
• Develop methods that go beyond prediction: assess smallest changes to fix issues and when results cannot be trusted.
• Release the model as open‑source software and support its use in regulatory dialogue.

Skills

Bayesian inference in PyMC/Stan/NumPyр

Python programming

ODE/dynamical systems modelling

English communication

Education

MSc in applied mathematics, computational science, physics, systems biology, or related quantitative field

Job description

Delft University of Technology (TU Delft) invites applications for a PhD candidate to build a computational model central to a Horizon Europe project on nanoparticle transport in human tissue. You will join the SYNERGIE consortium and lead in silico work within TU Delft, collaborating with international partners.

The role involves Bayesian inference, Python programming, and developing predictive models of particle transport, with a start date in January 2027.