Research Fellowships – White-boxing Artificial Intelligence – Vienna (# of pos: 4)
Institute of Advanced Research in Artificial Intelligence
Vienna, Austria
vor 1 Tg.
source : Euraxess

We are building a unique environment of world-class researchers and industrial-scale real-world data openly shared with the scientific community.

We take a proudly interdisciplinary approach to bring together top talent from complementary fields, combining in-depth domain knowledge in AI, simulation, and geospatial analysis from academia and industry.

All our research is open and published. to contribute and join us. We organize and sponsor scientific visits, research exchanges, multi-month thematic focus programmes, and academic competitions such as

We expressly invite competitive applications for

  • Research Fellowships for young rising stars,
  • which should include a concise research plan (up to 5 pages) and a full academic CV. Research Fellowships are endowed with generous discretionary funds, and the complete freedom to pursue your ground-breaking academic research.

    Our remuneration packages are generous and include comprehensive family health care, substantial pension plans (EU-wide), relocation support, etc.

    Offer Requirements

  • REQUIRED EDUCATION LEVEL Computer science : PhD or equivalent Mathematics : PhD or equivalent
  • Skills / Qualifications

    Key Research Areas

    The Institute pursues both basic and applied research in Artificial Intelligence.

    Critical analyses and fundamental studies seek novel insights that shape the future of our society.

    Key areas of research include

  • We train in simulators by reinforcement learning or other meta-learning strategies to study climate change models, smart cities, sustainable mobility, fleet management, etc.
  • We investigate the reliability and interpretability of modern machine learning models. We focus on high impact questions such as predicting patterns in urban traffic or large-scale rainfall, assessing climate change, biomedical image classification, and helping discover new drugs.
  • We work towards a rigorous understanding of the success of the latest black-box’ algorithms that is also intuitive to humans.
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