Post-Doc Position: Mechanistic Modelling for Personalised Medicine

We seek to establish mechanistic modelling in personalised medicine. Mechanistic modelling enables the integration of both prior knowledge about regulatory feedbacks and the physical-chemical constraints posed by diffusion-based transport into a computational framework. Unlike typical statistical models, mechanistic models represent the biomedical and clinical knowledge about the regulatory processes. Similar to clinicians in their daily work, such models can operate based on clinical datasets that are too small for machine learning approaches. But unlike clinicians, the models can encompass more information, are more consistent, and can use mathematics to predict the non-linear dynamical behaviour based on the known regulatory interactions. In combination, mechanistic computational models are promising approaches to personalise and guide both patient data collection and treatments based on the few patient data points that can be routinely collected in the clinic.

Project Background
Reproductive medicine, and IVF in particular, offer an ideal case study to establish mechanistic modelling for personalised medicine as the regulatory feedbacks have been defined, and IVF is well established both in human patients and in livestock such that large animal models, in particular cows, can be used to fill any gaps in human datasets.

Duties/Job description
You will focus on the development of predictive mathematical models that integrate literature data and newly generated data from farm animals and patients (3D images, hormone levels, gene expression time series) into a consistent framework that will allow us to provide data- and model-driven guidance regarding the best IVF treatment schedule. In collaboration with a PHRT-funded PhD student, the mathematical model will be carefully tested in clinical settings and then integrated into software for clinical use.

Your Profile

• You are experienced in building mechanistic, predictive mathematical models based on biological data.
• You are interested in image processing, finite element methods, and software engineering, and the female endocrine and reproductive system.
• You are self-motivated and enthusiastic about leveraging mechanistic modelling for personalised medicine, advancing FemTech, and contributing to the establishment of a strong personalised medicine/personalised health research community in Switzerland, for the eventual benefit of patients and citizens.

We offer
This is an interdisciplinary Post-Doc position funded by a 3-year grant from the Personalised Health and Related Technologies (PHRT) program. You will work in the group of Prof Iber (ETH, computational biology), and collaborate with Prof Ulbrich (ETH, animal physiology), and Prof De Geyter (University Hospital Basel, reproductive medicine).

The preferred starting date is January 2023; the exact starting date can be negotiated.

You can expect:

• an internationally highly competitive salary and further benefits
• excellent working conditions, with the possibility to work partly in the home office
• a friendly and collaborative working environment
• the opportunity to translate science to the clinic
• support for career development after the PHRT-funded Post-Doc via suitable academic fellowships such as SNF Ambizione, or an ETH Pioneer Fellowship to commercialise the results

Curious? So are we.
We look forward to receiving your online application with the following documents:

• Letter of Motivation
• CV
• Diplomas & Transcripts
• contact details of 2 referees

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Further information about the group can be found on our website. Questions regarding the position should be directed to Prof Iber by e-mail: iberd@ethz.ch (no applications).