PhD in Bayesian Modeling and Counterfactual Methods for Nanoparticle Transport in Human Tissue

Original title: PhD Position Bayesian Modeling and Counterfactual Methods for Nanoparticle Transport in Human Tissue

• 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: for a particle that behaves badly, what is the smallest change that would fix it, and when should that answer not be trusted.
• Release the model as open-source software and support its use in regulatory dialogue.
• MSc in applied mathematics, computational science, physics, systems biology, or a closely related quantitative field.
• 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.
• Continuous optimization or optimal control.
• Causal inference or counterfactual reasoning.
• Surrogate modelling, Gaussian processes, or emulation.
• Physiologically based pharmacokinetic modelling or systems pharmacology.
• Experience packaging and documenting scientific software.
• No background in toxicology, nanomaterials, chemistry or biology is required.
• #EUfunded This is an EU funded project, named SYNERGIE with project number 101288129 within European Union's Horizon Europe research and innovation program
• You can apply online. We will not process applications sent by email and/or post.
• Please do not contact us for unsolicited services.