Internship in mathematical modelling for digital health

Aixial Group is an international Clinical Research Organization providing clinical trial services to its clients in the pharmaceutical, medical devices, biotechnology, nutritional and cosmetics industries.

 

With a strong reputation for our expertise and flexibility, Aixial Group offers end-to-end clinical trial services, functional service provider models, business process outsourcing models, as well as expert consulting and advice.

More info, please visit our website https://www.aixialgroup.com/

 

Are you passionate about mathematical modelling and the development of computational methods to address complex problems involving heterogeneous and dynamic data in a health research context? If yes, this internship opportunity is for you.

 

Context

 

We are seeking a motivated and talented intern to join our research and innovation team for a 6-month period. Working closely with our scientific experts, you will contribute to the development of mathematical and probabilistic approaches for the integration of cumulative effects and the time dependence of environmental factors into predictive modelling frameworks for health-related applications.

 

Goals & tasks

 

The internship will focus on methodological challenges related to the modelling of cumulative exposures, the handling of uncertainty, and the integration of heterogeneous data sources into coherent analytical frameworks.

 

Particular attention will be given to probabilistic methods, dynamic representations, and modelling strategies capable of capturing delayed, repeated, or interacting effects over time.

 

You will be actively involved in the full research cycle: literature review, formulation of modelling hypotheses, development and implementation of methods, integration of heterogeneous data, validation, interpretation of results, and dissemination of findings.

  • Student in applied mathematics, mathematics for life sciences, biostatistics, data science, mathematical modelling, or a related field.
  • Strong background in probability, statistics, or mathematical modelling.
  • Experience in programming using Python, R, or other scientific computing languages.
  • Strong interest in dynamic systems, uncertainty modelling, and data integration.
  • Ability to formulate, test, and compare methodological hypotheses.
  • Analytical mindset and strong problem-solving skills.
  • Ability to work independently and collaboratively in a multidisciplinary team.
  • Excellent communication and presentation skills.