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Field
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positions are attached to four different collaborative projects with related but distinct goals, all making use of our expertise in squeezing-based entangled states of light: ClusterQ (ERC): A platform for
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. Research Environment The project is in collaboration with two partners: (i) IDCOM at the University of Edinburgh, which develops theory, algorithms and hardware for the next generation of signal processing
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aims to optimize the operations (serving) of AI by developing algorithms that manage compute, network, and storage resources in a carbon-efficient way while supporting long-term benefits
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for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 19 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different
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different aspects of evolvability – the ability of organisms to evolve. We are interested in developing computational and mathematical tools to understand and quantify evolvability while exploring its
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: Proficiency in algorithm development for autonomous systems. Experience with ROS2, UAV simulation (e.g., AirSim), and real-time system integration. Strong programming skills in C++ and Python. Excellent
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data and the algorithms need to comply with local legislation. We seek a postdoctoral researcher who is interested in Developing Data-driven control solutions for medical mobile applications. Investigate
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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want The postdoctoral research associate will be working on developing machine learning/artificial intelligence algorithms for various applications, including energy systems, health systems, and marine
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in their computation. We want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with several