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Field
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to develop a data-efficient and uncertainty-aware framework for pigment mapping in Cultural Heritage. Heterogeneous spectral and imaging data from different sensors will be jointly exploited, including X-ray
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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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that does not reflect local reality and thereby create unfair comparisons. Your work will include measurements of greenhouse gases (CO2, CH4, N2O) using drones and various sensors, as well as remote sensing
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mobility and vulnerability to falls. The project will combine physiological sensory biomarkers with postural control, gait analysis and real-life mobility measurements obtained using wearable sensors and
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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of electron spin pairs in chiral molecules created by light using diamond-based quantum sensors. Spin dynamics of these radical pairs are central both in biology and for quantum information science applications
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multimodal data. Your responsibilities include: Developing and applying machine learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing
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. Your main responsibilities will include: Fly drones and process data of multispectral, thermal and LiDAR sensors Implement approaches to integrate drone data into an existing agroecosystem data
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description The functioning of cities depends more than ever on urban infrastructures like transportation networks, power grids, water networks, Internet of Things sensors, and analytics platforms that gather
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welfare are monitored on farms. However, many current monitoring systems rely primarily on visual or activity-based sensors, meaning important behavioural and physiological signals may be missed. Sound