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selectivity is the first important barrier to overcome in order to perform quantitative analyses for each pollutant and avoid ionic interference between the different sensors used in the project. Sensor
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to have good knowledge of computer science, mathematics, algorithms, and programming. Knowledge and experience in artificial intelligence and machine learning is expected, but not required. Knowledge and
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advantages. We will provide the necessary hardware and software for the real-time control of the machine, but the candidate will be responsible for developing and implementing the control algorithms. A working
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reliable and reproducible measurements across different assays. In this PhD project, you will develop RMPs and reference materials (RMs) for several protein TMs to enable harmonized and reproducible
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is not a standalone concept and has close connections to diversity, transparency and bias. In this position, the PhD candidate will work on algorithmic fairness in job recommender systems
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learning algorithms, and design of optical communication networks or power consumption and energy saving. The synergies of MATCH consortium act together to enable the thirteen DCs to become the next
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optimization to find the optimal set of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised learning techniques (e.g
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different EU countries. All 15 Ph.d. projects are within the overall theme of neuromorphic computing and analog signal processing, targeting applications in the fields of communication, sensing
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universities, one research center and two companies. The project has partners from eight different EU countries. All 15 Ph.d. projects are within the overall theme of neuromorphic computing and analog signal
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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