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activities in the following areas: · Selection, integration and calibration of thermal and composition sensors for melt pool monitoring in advanced DED-LB process control · Implementation of thermal (gradient
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the improvement of the wetting/during algorithm in TELEMAC2D, including the effects of vegetation. Modelling the SPM turbidity in 3D (using TELEMAC3D) in front of the Belgian coast, validated with 3D remote sensing
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neuromorphic ultra-low-power active sensor readout and processing at the edge. The chip design will enable online learning capabilities, aiming at modulating the spatio-temporal filtering properties with
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damping systems) will be tested. The research will be mainly experimental, using a dedicated sloshing table equipped with advanced sensors and flow visualization. The candidate will also contribute
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healthcare for FOG; identify technical requirements for the development of just FMs-based solutions for FOG. Develop a data collection method embedding wearable IMU sensors for monitoring FOG in everyday life
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for Biomarker Discovery: Develop and fine-tune machine learning algorithms for biomarker discovery, cancer classification, and exploring omics features. Collaborative Research: Work in a multidisciplinary setting
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field. Research Field: Fluid dynamics, control engineering, or dynamical systems. Required Skills/Qualifications: Experience with experimental methods (laboratory setups, sensors, data acquisition
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theoretical background for MIMO related research, and have attended courses such as Information Theory, Signal and Systems, Modulation and Detection. Experience with OFDM or single carrier baseband algorithms
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and Saeys teams. In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit
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and engineer novel optical designs. You have proven experience with programming and a motivated interest in programming image analysis algorithms. You have proven experience, or show a motivated