Summer Internship | 2025

Intern / Student, Full or part-time · Vienna

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WE SEARCH FOR
Many of the most important challenges facing society today are unprecedented and so complex that they can only be solved by innovative and highly transdisciplinary approaches – complexity science. Complexity science integrates high-dimensional, real-world data analysis and computational modeling to deliver insights to the systems that underlie our world. Join us for an internship and gain first-hand experience with complex systems research and its foundational theories and tools.

Find more information and descriptions of available projects on the program page here.

NOTE: This is an unpaid position.
YOUR PROFILE
  • Students currently enrolled in a degree-granting Bachelors, Masters, or combined degree program from an accredited institution.
    • Bachelors students should have completed at least 2 years of study by the start of the internship.
    • Applicants planning to re-enroll in such a program within the next 12 months are also eligible.
  • Applicants with a facility for quantitative approaches.
    • Prior coursework in a quantitative discipline (e.g., social, natural, or information sciences, mathematics, engineering).
    • Programming experience or coursework in Python, R, or statistical platforms (an asset).
    • Proficiency in English.
  • Individuals whose goals align with those of the program.
    • This is a learning experience: no prior independent research experience or familiarity with complexity science methods required.
    • Remote participation is not possible. Accepted students must be in Vienna, Austria, for the duration of the program. 
  • We strongly encourage applications from women and individuals from historically excluded groups in the sciences.
  • Availability for 6 to 10 weeks, beginning on approximately June 23, 2025. We do not offer internships outside of the summer months of June-August.
WE OFFER
  • Independent Complexity Science Project:
    • Engage in a self-driven research project focused on complexity science. Available projects can be found here.
  • Individualized Training:
    • Receive personalized guidance and mentorship from dedicated project advisor(s).
  • Scientific Talks and Research Seminars:
    • Attend talks and seminars on cutting-edge research topics in complexity science.
  • Introductory Skills and Foundations Workshops:
    • Participate in workshops to build foundational skills and knowledge.
  • Social Activities:
    • Enjoy a select number of intern-only social events.
  • Project Presentation:
    • Present your research findings to the CSH community.
The core of the internship experience is the project and the close collaboration with the project advisor(s). The project advisors, along with other members of the research team, will help with technical aspects of the project and provide regular opportunities to discuss bigger-picture elements of the work. Advisors provide reading lists and additional learning resources so that students can enhance their knowledge and skills related to the project. Students will also have access to a range of workshops, seminars, and talks to help them gain knowledge about complexity science and current areas of research.
APPLICATION
Application for a summer internship at CSH is possible from November 1, 2024, through January 31, 2025 (midnight Vienna local time). Late applications cannot be accepted. 

Aplicants needs to submit: 
To input directly ​into the application ​system:
(1) biographical information
(2) general details of academic background
To upload:
(3) curriculum vitae or resume
(4) a cover letter that addresses the following points:
  • Describe any job experiences, coursework, hobbies, skills, degrees, certifications, or licenses that you have that might be relevant during this internship.
  • Describe your vision as a young scientist. What problem(s) would you like to solve? If your interest is in data visualization, what do you hope to achieve with your work?
  • Describe what interests you about a specific project and how it fits into your scientific vision. Describe how this internship could be useful towards realizing your scientific (or other) goals.
 
Candidates will be notified of their application status in early March.
About us
The Complexity Science Hub (CSH) is Europe’s research center for the study of complex systems. We derive meaning from data from a range of disciplines – economics, medicine, ecology, and the social sciences – as a basis for actionable solutions for a better world. Established in 2015, we have grown to over 70 researchers, driven by the increasing demand to gain a genuine understanding of the networks that underlie society, from healthcare to supply chains. Through our complexity science approaches linking physics, mathematics, and computational modeling with data and network science, we develop the capacity to address today’s and tomorrow’s challenges.
IHRE AUFGABEN:
Many of the most important challenges facing society today are unprecedented and so complex that they can only be solved by innovative and highly transdisciplinary approaches – complexity science. Complexity science integrates high-dimensional, real-world data analysis and computational modeling to deliver insights to the systems that underlie our world. Join us for an internship and gain first-hand experience with complex systems research and its foundational theories and tools.
Über uns
Das Complexity Science Hub (CSH) ist Europas Forschungszentrum für die Untersuchung komplexer Systeme. Wir leiten Bedeutung aus Daten verschiedener Disziplinen ab - Wirtschaft, Medizin, Ökologie und den Sozialwissenschaften - als Grundlage für handlungsorientierte Lösungen für eine bessere Welt. Seit unserer Gründung im Jahr 2015 sind wir auf über 70 Forscher angewachsen, angetrieben von der zunehmenden Nachfrage nach einem echten Verständnis der Netzwerke, die der Gesellschaft zugrunde liegen, von der Gesundheitsversorgung bis hin zu Lieferketten. Durch unsere komplexen wissenschaftlichen Ansätze, die Physik, Mathematik und computergestützte Modellierung mit Daten- und Netzwerk-Wissenschaften verknüpfen, entwickeln wir die Fähigkeit, die Herausforderungen von heute und morgen anzugehen.   
   
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