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available in the further tabs (e.g. “Application requirements”). Objective This programme offers you the opportunity to do a bi-national doctoral degree at your home university and at a university in Germany
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at Fraunhofer ITWM are machine learning, artificial intelligence, renewable energies and sustainability. In addition, next-generation computing and quantum computing are future-oriented issues that our
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are described here . Research Environment The University of Konstanz has been successful in the German Excellence Initiative and its follow-up programme, the Excellence Strategy and is among the most highly
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with foreign partner organisations. The scholarships are funded by the Federal Ministry of Research, Technology and Space. Who can apply? The programme is aimed at university teachers and experienced
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offer A vibrant research community in an open, diverse and international work environment Scientific excellence and extensive professional networking opportunities A structured PhD program with a
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automation in industry. Disruptive technologies such as artificial intelligence and quantum computing play a key role here. Fraunhofer IKS is conducting research to ensure that these applications are reliable
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. As a model intercomparison project, TIPMIP brings together international modeling groups to jointly develop and run computer simulations. In this way, TIPMIP creates a unique data basis for
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Sensors play a pivotal role in modern analytical sciences, particularly in the development of high-performance platforms for the detection of biological and chemical analytes. Applications span health care, environmental monitoring, breath diagnostics (breathonics), and industrial safety....
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qualified women. About the position The position contains both teaching duties and participation in research projects. The research project topics focus on improving object recognition through computer vision
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. Applicants should have a background in population genetics/genomics, molecular ecology, biodiversity informatics, or a related field. Experience with large-scale data analysis is essential. Additional