Johan

We are hiring Posdoctoral Fellows!

The Center for Social and Biomedical Complexity (CSBC) at Indiana University Bloomington is accepting applications for one or more full-time non-tenure track postdoctoral fellows to conduct interdisciplinary research in Complex Networks and Systems applied to various social, ecological, biological, medicine and health problems. The expected start date for the appointments is February 2020.

Candidates interested in conducting research in urban community-environment systems, or network science methods to analyze and visualize information relevant for epilepsy and other chronic diseases are encouraged to apply. The appointments are full-time for 12 months, with potential to be extended an additional year subject to funding and satisfactory performance. We offer a competitive salary with generous benefits.

The postdocs will join a dynamic and interdisciplinary team that includes systems scientists, biologists, computer scientists, and social scientists. The postdocs will work with Prof. Luis M Rocha and Prof. Johan Bollen.

Basic Qualifications: A PhD is required in Complex Systems, Network Science, Computer or Computational Science, Computational Biology, Applied Mathematics, Physics, Statistics, Artificial Intelligence or related field; a strong background in analysis and modeling of complex systems and networks; and solid programming skills necessary to handle big data and develop large scale simulations. ABD (all but dissertation) candidates may apply but will need the Ph.D. prior to start.

Applications received by January 29, 2020 will receive the fullest consideration. We will continue accepting applications until the positions are filled. Please review the application requirements and apply online. Questions may be sent to: luddyjob+postdoc-lr@indiana.edu or Professors Bollen and Rocha.

Indiana University is an equal employment and affirmative action employer and a provider of ADA services. All qualified applicants will receive consideration for employment without regard to age, ethnicity, color, race, religion, sex, sexual orientation, gender identity or expression, genetic information, marital status, national origin, disability status or … continue reading.

CSBC Awarded Project to develop Resilient Community-Environment Interactions in Urban Waterways

Prepared for Environmental Change LogoIn a collaboration with Professors Heather Reynolds (IU Biology) and Gabriel Filippelli (IUPUI, Center for Urban Health) of the Environmental Resilience Institute, CSBC Professors Bollen and Rocha were awarded a grant for project “A River Runs Through It: Restoring Biodiversity and Empowering Resilient Community-Environment Interactions in Urban Waterways.” This 2-year project is part  of the Prepared for Environmental Change (PfEC) Grand Challenge initiative at Indiana University.

The project integrates environmental, social, and complex adaptive systems sciences with stakeholder values to study an urban watershed in transition, developing strategies to enhance its resilience to shocks–both slow (i.e., environmental change) and rapid (i.e., sewage diversion). CSBC will develop a  socio-ecological complex adaptive system analysis to identify, visualize, and model the dynamic relationships between public and private stakeholders and watershed biophysical variables.… continue reading.

Bollen social media study shows how affect labeling can help moderate emotions

Your mother always told you that if something was bothering you, you should talk about it. It would make you feel better. Turns out she was right, and researchers at the School of Informatics, Computing, and Engineering have the science to prove it. Johan Bollen, a professor of informatics and computing, leads a team that analyzed the Twitter feeds of tens of thousands of users to study how emotions change before and after they were explicitly stated. In the study, “The minute-scale dynamics of online emotions reveal the effects of affect labeling,” published in the journal Nature Human Behaviour, Bollen and his colleagues used algorithms to measure how the positivity or negativity of tweets change before or after a user explicitly expressed having an emotion, e.g. saying “I feel bad” or “I feel good.” Their study not only reveals how emotions evolve over time, but also how their expression may change them, and how these changes differ between men and women.… continue reading.