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.

Fixed-term contract: 18 months. Upon a positive evaluation, an extension of 2.5 years will follow.

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

Maastricht University

Why work at Maastricht University?

At Maastricht University (UM), everything revolves around the future. The future of our students, as we work to equip them with a solid, broad-based foundation for the rest of their lives. And the future of society, as we seek solutions through our research to issues from all around the world. Our six faculties combined provide a comprehensive package of study programmes and research.  

In our teaching, we use the Problem-Based Learning (PBL) method. Students work in small groups, looking for solutions to problems themselves. By discussing issues and working together to draw conclusions, formulate answers and present them to their peers, students develop essential skills for their future careers.

With over 22,300 students and more than 5,000 employees from all over the world, UM is home to a vibrant and inspiring international community.

Are you drawn to an international setting focused on education, science and scholarship? Are you keen to contribute however your skills and qualities allow? Our door is open to you! As a young European university, we value your talent and look forward to creating the future together.  

Click here for more information about UM.

School of Business and Economics

The School of Business and Economics (SBE) is one of Maastricht University’s six faculties. Our mission is to contribute to a better world by addressing societal problems, by co-creating knowledge and developing team players and leaders for the future.

By joining SBE, you will be part of the 1% of business schools worldwide to be Triple Crown accredited (EQUIS, AACSB and AMBA). You will join an inspiring and international environment, where learning and development is priority.