Financial and Actuarial Mathematics, TU Wien, Austria TU Wien FAM


The Complexity Science Hub Vienna is a young research institution dedicated to a deeper quantitative and predictive understanding of complex systems for the betterment of society. We provide an exciting, creative, bureaucracy-free environment for open-minded visionaries who want to make a change and are brave enough to step out of mainstream science.

3-year PhD Position in Graph Learning on Financial Network

  • Graph Learning on Financial Networks
  • € 34.500 gross/year*, part time (30h)
  • * according to the standard personnel costs and salaries of the Austrian Science Fund FWF

We search for

An excellent young scientist with a masterís degree or equivalent in computer science, physics, mathematics, statistics, engineering, or related fields. Basic knowledge of network analytics and machine learning techniques is a plus. The successful candidate will investigate graph learning techniques on financial networks, especially those constructed from cryptoasset transactions, and learn about the impact of cryptoassets in the real economy. The applicant must be critical, open-minded, self-motivated, and collaborative.

Your profile

You should have some research experience at a master's thesis level or equivalent. You must be proficient in English, both written and spoken. We expect you to be able to independently carry out data-intensive research, as exemplified by your past works that showcase your strong programming, modeling, and mathematical skills.

We offer

A fully funded 3-year Ph.D. position in an exciting research environment at the Complexity Science Hub Vienna. You will work with Bernhard Haslhofer and members of the CryptoFinance group, and Maria Del Rio Chanona of the Economics group. The successful candidate will have the opportunity to work at a highly interdisciplinary interface and benefit from a dynamic, collaborative, and international research environment.

Your responsibilities

The successful candidate will assume a pivotal role in driving research in the designated area. You will be responsible for the conception, implementation, and publication of research initiatives, in partnership with other researchers both within the Hub and external to it. Your work will involve close collaboration with experts in computer and network sciences to explore algorithmic measurement techniques and financial ecosystem models, with the aim of gaining insights into the intricate financial interdependencies among various stakeholders and potential systemic risks.


Please send your application material to with the subject line "PhD Graph Learning on Financial Networks". The application includes a curriculum vitae, a list of publications, a brief research statement (why you think you are an excellent candidate in this topic and what research you would like to do with us), and any additional material that helps us to understand that you are research-oriented, technically skilled, and creative. Please also include names and full contact addresses of at least two individuals who are willing to write a letter of recommendation for you if we request it. We will start evaluating applications immediately. The position is available from September 2023 and will remain open until filled. We will review the first round of applications by June 15th, 2023.

CSH is committed to the principle of equal employment opportunity for all applicants. All employment decisions are therefore based on job requirements, qualifications, merit, and organizational needs. We strongly encourage individuals from underrepresented groups to apply.

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Name: Complexity Science Hub Vienna
Telefon: 0159991600

(Link to Job Offer)

At the application please refer to the FAM-jobs webpage.