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Field
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university/spinout environment. This is a unique opportunity to work at the forefront of applied research and innovation, helping translate novel control algorithms and hardware prototypes into real-world
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. Development of real-time optimization algorithms and model predictive control (MPC) strategies for adaptive process management. Addressing data sparsity and data quality issues in industrial process data
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change, climate change attitudes, and risk under environmental heat stress by combining psychological profiling, biological lab data, physiological time series, and sensor data. The postdoc will play a
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technologies. As a part of the DiSTAP program we will develop new sensors for crop management and deepen our understanding of plant biology. DiSTAP is one of the five Interdisciplinary Research Groups (IRGs
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-class S&T products for sensitive national security missions. The selected candidate will support research efforts in signal processing and analysis, with an emphasis on the development of novel algorithms
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by combining psychological profiling, biological lab data, physiological time series, and sensor data. The postdoc will play a leading role in developing and implementing predictive algorithms designed
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developments in sensor design, dataset transmission, data analysis, and numerical modeling to distinguish between normal and abnormal features. Here, the goal is to develop machine learning algorithms
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and tomographic radar capabilities. Our team is responsible for the algorithms which derive the biomass data product. The post-doc project is about extending the biomass algorithm to also include data
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length scales Develop machine learning algorithms to support process optimization, predictive modeling, and intelligent manufacturing control Integrate simulation tools with in-situ sensor data from
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measurements Calibration of materials as sensors to measure temperature, oxygen, and pH values in cells Development of models based on artificial intelligence algorithms to interpret luminescence signals Study