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applying different multivariate calibration strategies and machine learning approaches. Finally, the variation of the sensor measurements will be studied in relation to the cow’s health and combined with
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of analytical chemistry and machine learning. For more information please contact Prof. dr. Deirdre Cabooter, mail: deirdre.cabooter@kuleuven.be . Where to apply Website https://www.kuleuven.be/personeel/jobsite
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background in AI-enabled signal processing and machine learning algorithms, have experience with embedded platforms (e.g., NPU, FPGA, ARM Cortex-M), be proficient in programming languages like C, C++ and
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and metrics may also support the development and evaluation of computer vision models for automated performance assessment and feedback. Ultimately, this research will contribute to the creation
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develop and evaluate semi automated machine learning techniques for content analysis adapted to handwritten documents and early printed books. These approaches will build upon existing optical character
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analog electronic accelerators. You’ll collaborate closely with a multidisciplinary team of machine learning experts, software developers, computer scientists, fabrication specialists, and experimentalists
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in dynamical systems modeling (ODEs) and machine learning and very strong programming skills (Java, Python). A background in evolutionary genomics research is a strong plus, as is previous experience
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the discipline of bioinformatics, data analysis of large-scale (bio)medical data, applications of artificial intelligence and machine learning. You contribute to high-quality teaching in bachelor and master years
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brings complementary and/or additionally new expertise including AI/Machine Learning-based methodologies that can be developed for virtual ligand screening, reverse virtual screening (target fishing) and
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or integrate with larger epidemiological databases You are experienced in machine learning models to interpret complex data types The official administrative language used at KU Leuven is Dutch. If you