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Project advert Modern artificial intelligence (AI) increasingly relies on combining multiple sources of information, such as sound, motion, images, and sensor data, to achieve robust and intelligent
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in the live organism to study e.g. immune responses or cancer development. Moreover, using molecular sensors we also aim to read out cellular functionality in vivo e.g. metabolic reprogramming in
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depletion, toxic algae, and pollutants. This natural sensitivity makes them powerful bio-sensors for environmental monitoring, capable of providing early warnings of ecosystem stress. However, harnessing this
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applications. The focus is on intelligent sensor systems that efficiently acquire, process, and evaluate data—even under demanding operating conditions. Working in interdisciplinary teams, we combine scientific
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simulation of scenarios with different materials and geometries. - Support the development and implementation of signal and image processing algorithms, including fast inversion techniques, FFT, and nonlinear
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Application deadline: 15 March 2026 Apply now This position focuses on fundamental open problems in algorithm design and computational complexity. The main theme will be a unifying theory of algorithmic power
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monitoring. Conventional early-warning systems, such as optical/IR cameras, satellites, human observers, and dense sensor networks, either provide limited warning time, depend on clear visibility, or are too
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networks, Internet of Things sensors, and analytics platforms that gather data from those infrastructures, as well as telecommunications networks. To fully support the operation of cities, telecommunications
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Distributed radar systems comprise a coherent network of spatially distributed sensors that can be independently transmitting, receiving, or both. By acting in unison, rather than in isolation
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and to better understand the physiological mechanisms of resistance to abiotic constraints. The acoustic signature, integrated into the algorithm controlling the autonomous acoustic sensors, will