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to transform EO data into reliable, transparent and actionable intelligence is critical. You will explore novel approaches for exploiting multi-sensor satellite data — such as optical, SAR, thermal, and
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6 Feb 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Computer science » Programming Mathematics » Algorithms Mathematics » Discrete mathematics
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interaction and/or surface flux computation, including familiarity with bulk flux algorithms and observational QA/QC procedures. Experience with processing, analyzing, and interpreting multi sensor
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will address the intricate challenge of enabling AI to learn continuously and collaboratively from wearable or mobile sensor data without compromising user privacy. Your efforts and collaborations with
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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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continuously and collaboratively from wearable or mobile sensor data without compromising user privacy. Your efforts and collaborations with other European Union partners will contribute to the advancement
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a primary emphasis on designing smart algorithms to trigger non-invasive blood pressure (NIBP) measurements at critical times. This will involve leveraging physiological sensor signals such as the
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intelligence (AI) and machine learning(ML). Duties This position combines knowledge of the Earth observation (EO) domain (EO instruments, EO data, EO algorithms, modelling, etc.) and AI/ML, as well as data
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and spatially complex nature of MRI signals. Each MRI examination involves multiple pulse sequences, with signal acquisition being sensitive to coil placement, sensor geometry, B0/B1 inhomogeneities
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to source localization based on microphone arrays or distributed sensors. This PhD project will focus on the development of novel methods and algorithms for airborne noise source localization in generic urban