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Elma Dervic

Postdoctoral Researcher

Complexity Science Hub Vienna

Vienna, Österreich

Meine Organisation

Complexity Science Hub Vienna

Complexity Science Hub Vienna

Forschungseinrichtung

Wien, Wien, Österreich

We are Europe’s Research Center Translating Data into Solutions for a Better World Since 2016, the Complexity Science Hub has conducted independent science for a better understanding of our complex world. We advance progress by extracting meaning from the enormous amounts of data representing our planet in its various dimensions: the economy, human migration, health and disease, climate crisis, social values, urban development, conflict, and more. Our research approach lets us see the world in a new way, as interconnected, dynamic, co-evolving, extensive networks, that allow us to understand these complex systems and identify their weaknesses and strengths. Using this knowledge, we can make evidence-based statements about how complex systems will respond to change and propose realistic interventions to move them in a positive direction for society. In other words, we want to find solutions for a better world.
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Über mich

Elma Dervic is a postdoctoral fellow at the Complexity Science Hub and the Medical University of Vienna.

She completed her PhD at CSH and the Medical University of Vienna in January 2023. Elma holds a bachelor’s and a master’s degree in electrical engineering from the University of Montenegro, which she earned in 2015 and 2017, respectively. Before joining CSH, Elma was a junior researcher at the First Center of Excellence (BIO-ICT) at the University of Montenegro.

Elma is a co-founder of BeeAnd.me, a venture-funded IoT startup that uses technology to help beekeepers and bees through data science. She is known as a 3-time “share positive impact” TEDx speaker. Elma’s research interests include machine learning, network science, and everything impactful related to data science.

Currently, she is working with medical data on unraveling disease trajectories. Furthermore, she is working on trading data to analyze systemic risk in supply chains.

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