PhD in Network Science

Maastricht University
December 18, 2022
Contact:N/A
Offerd Salary:€ 2.541
Location:N/A
Working address:N/A
Contract Type:Other
Working Time:Full time
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Job Ref.:N/A
Job description

Are you a highly motivated researcher and excited by interdisciplinary research on developing new methods for analysing complex dynamic network systems using uncertain data? Apply for a PhD position in the Data Analytics and Digitalisation department at Maastricht University. 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, neuronal activity of the brain, as well as the way we interact in society. Key to understanding these complex systems is 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 systems, (ii) determine the limits of network reconstruction in uncertain data, and (iii) apply these methods to real-word systems.

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Requirements

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 in a team, and also be open to collaborations across scientific fields.
  • Have excellent verbal and written communication skills in English.
  • Prior experience with Bayesian statistics and/or network science would be advantageous

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    Conditions of employment
  • We offer a rewarding career at a young university in the heart of Europe, with a distinct global perspective and a strong focus on innovative research and education;
  • The terms of employment of Maastricht University are set out in the Collective Labour Agreement of Dutch Universities (CAO), supplement with local UM provisions. For more information on terms of employment, please visit our website www. maastrichtuniversity.nl > About UM > Working at UM
  • The salary will be set in the PhD-candidate salary scale of the Collective Labour Agreement for Dutch Universities (€ 2.541 gross per month in the first year up to € 3.247 gross per month in the fourth year). On top of this, there is an 8% holiday allowance and a 8.3% year-end allowance;
  • We offer an attractive package of fringe benefits such as reduction on collective health insurance, substantial leave arrangements, optional model for designing a personalised benefits package and application for attractive fiscal arrangements for employees from abroad.
  • We offer a full-time employment contract as a PhD candidate. The employment contract will be for a period of 1,5 years and will be extended for another 2,5 years after positive evaluation.
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    Employer Maastricht University

    Maastricht University is renowned for its unique, innovative, problem-based learning system, which is characterized by a small-scale and student-oriented approach. Research at UM is characterized by a multidisciplinary and thematic approach, and is concentrated in research institutes and schools. Maastricht University has around 20.000 students and 5.000 employees. Reflecting the university's strong international profile, a fair amount of both students and staff are from abroad. The university hosts six faculties: Faculty of Health, Medicine and Life Sciences, Faculty of Law, School of Business and Economics, Faculty of Science and Engineering, Faculty of Arts and Social Sciences, Faculty of Psychology and Neuroscience. For more information, see https:// www. maastrichtuniversity.nl/

    School of Business and Economics SBE is the youngest economics and business faculty in the Netherlands with a distinctively international profile. It belongs to the 1% of business schools worldwide to be triple-crown accredited (EQUIS, AACSB and AMBA). SBE strongly believes in close connections with its academic partners and societal stakeholders, with its students and alumni, and with businesses and organisations in the Limburg Euregion, the Netherlands, Europe and the rest of the world. SBE is committed to increasing the gender diversity of its academic community and wants to stimulate the creation of a more diverse and inclusive community. For more information see: https: // www. maastrichtuniversity.nl/about-um/faculties/school-business-and- economics

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    Department Data Analytics and Digitalisation (DAD)

    The department of Data Analytics and Digitalisation (DAD) connects data science (mathematics, statistics, computer science, artificial intelligence) with business and economics research (finance, accounting, marketing, information management, operations, micro- and macroeconomics, policy design). We are responsible for conducting top-level research in data science for business and economics, ranging from fundamental theoretical studies to applied industrial projects.

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    Additional information

    More information on this vacancy can be obtained from Dr. Leto Peel (l.peel@maastrichtuniversity.nl)

    To apply at this vacancy, press the button ‘apply now' to see the application procedure.

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