#TRANSFORMINGTOMORROW: MAKE A DIFFERENCE WITH US.
IT:U — the Interdisciplinary Transformation University in Linz, Austria offers Tenure-track and Tenured Professorships in interdisciplinary approaches to Data Science.
Shape the future with us as a
Professor of Data Science
It has been said that data will be to the 21st century what oil had been to the 20th. While scientific domains such as medicine, life sciences, engineering, social sciences, humanities etc. struggle to work with the data they have access to, data scientists often lack the relevant knowledge on domains. Only few people are currently able to bridge this gap and address the relevant questions around what to measure, how to critically reflect on, how to collect, how to process, and how to make sense of data with the actual use cases. With new, interdisciplinary professorships combining data sciences with specific domain knowledge, IT:U seeks to engage deeply with data as the key driver of digital transformation, never losing sight of its rich context, meaning, and power in the real world.
To achieve this degree of interdisciplinary and transformational research, we welcome applicants from a wide range of academic backgrounds that are connected to or relevant to data science.
Importantly, we aim to strengthen the diversity of perspectives within the data sciences which will recognize the plurality of our society. As such, we particularly encourage researchers with typically under-represented backgrounds to apply at IT:U, e.g., in terms of gender, (dis)ability, social environment, cultural or ethnic heritage, etc.
WE ARE LOOKING FOR YOU
We welcome outstanding applicants with research and teaching experience who have demonstrated impact in innovative areas of Data Science and its intersection to other disciplines. The successful candidates will join a dynamic academic environment to lead research, foster collaboration, and contribute to teaching in fields critical to the future of data science.
Candidates can come from a wide range of academic backgrounds that are connected to or relevant for data science, data thinking, and the intersection between data science and other disciplines such as for example (this is a non-exhaustive list):
- Critical Data Studies — bridging technological expertise with the systematic study of social, cultural, and ethical implications of data driven socio-technical systems.
- Theoretical Foundations of Data Science — engaging with areas such as mathematics of data science, statistical learning, or specific topics like topological data analysis and causality.
- Data Science in Biological Systems — using data, computational methods, and mathematical models to study biological processes at various scales, in areas such as systems biology, microbiome ecology, single-cell genomics, and metagenomics. This includes potentially integrating experimental data and/or extensive databases and enabling advancements in both basic and applied biological research.
- Data Science in Health, Wellbeing, and Medicine — engaging with data science in areas such as Health Data Science or big data applications around health, wellbeing, and medicine.
- Data Science in Climate Change and Environmental Systems — using data to understand complex environmental systems and predict the effects of climate change such as flooding and storms.
- Data Science in Human Mobility — using data science to better understand human mobility and reinterpret the ways in which human beings move around urban and rural areas.
- Data Platforms & Infrastructures — designing, integrating, evaluating, and improving data sharing infrastructures within organizations, across organizations (platform economy), national, European, and global settings.
