PhD Candidate in Network Science | School of Business and Economics | 1.0 FTE

Research / Academic

You will develop methods to reconstruct time-evolving networks from uncertain and indirect observational data and apply these to real-world complex systems.

 Complex systems play an important role in many aspects of our lives, including technological systems such as the world wide web, telecommunications and power grids, biological systems of metabolic interactions and neuronal activity of the brain, as well as the way we interact in society. Key to understanding these complex systems are the use of networks that allow us to analyse the system as a whole, rather than as a collection of independent units. Most empirical studies of networks, as well as the methods they employ, assume that the network data we are given represent a complete and accurate picture of the nodes and edges in the system of interest. However, data collected on real-world systems are typically prone to noise, errors, omissions and inconsistencies. This project aims to better understand the impact of these uncertainties on the analysis of time-evolving complex systems and develop statistical models and inference methods that are robust to noisy, error-prone or missing data.

You will develop models of uncertainty to study the effects of noise and missing data on temporal network analysis. The aims of the project are to (i) develop methods to reconstruct networks from noisy and indirect observations of dynamic complex biological systems, (ii) develop efficient algorithms for scalable inference, and (iii) apply these methods to real-word biological systems.


The candidate will be responsible for developing novel probabilistic network models and using Bayesian inference to fit these models to real-word systems. The candidate should therefore:

  • Hold a Master degree in Statistics, Mathematics, Computer Science, Physics, or another domain with a strong computational and mathematical background;
  • Have strong  programming skills;
  • Have a passion for interdisciplinary research;
  • Be motivated to work independently and within a team and be open to collaborations across scientific fields;
  • Have excellent verbal and written communication skills in English;
  • Prior experience with Bayesian inference, network science and/or computational biology would be advantageous.

Salary Benefits:

As a PhD Candidate at The School of Business and Economics you will be employed by the most international university of the Netherlands, located in the beautiful city of Maastricht. In addition, we offer you:

  • Good employment conditions. The position is in grated in PhD salary scale with corresponding salary ranging from € 2.770 to € 3.539 gross per month (based on a full-time employment of 38 hours per week). In addition to the monthly salary, an 8.0% holiday allowance and an 8.3% year-end bonus are applicable;
  • An employment contract for a period of 18 months with a scope of 1.0 FTE. Upon a positive evaluation, an extension of  2.5 years will follow;
  • As Family-friendly University, we offer flexible working hours and the possibility to work partly from home if the nature of your position allows it. You will receive a monthly commuting and internet allowance for this. If you work full-time, you will be entitled to 29 vacation days and 4 additional public holidays per year, namely carnival Monday, carnival Tuesday, Good Friday, and Liberation Day. If you choose to accumulate compensation hours, an additional 12 days will be added. Furthermore, you can personalize your employment conditions through a collective labor agreement (CAO) choice model;
  • As Maastricht University, we offer various other excellent secondary employment conditions. These include a good pension scheme with the ABP and the opportunity for UM employees to participate in company fitness and make use of the extensive sports facilities that we also offer to our students;
  • Last but certainly not least, we provide the space and facilities for your personal and professional development. We facilitate this by offering a wide range of training programs and supporting various well-established initiatives such as 'acknowledge and appreciate';
  • The terms of employment at Maastricht University are largely set out in the Collective Bargaining Agreement of Dutch Universities. In addition, local provisions specific to UM apply. For more information, Click here



Work Hours:

38 hours per week


Tongersestraat 53