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fusion, machine learning, and systems modelling. We are at the forefront of method development towards large-scale data analysis and modeling of biological systems. Together with a wide range of
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also include generative or predictive modeling of dynamic radar scenes. The project combines methodological machine learning research with experiments on real automotive sensor data. You will have access
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have: MSc in engineering or similar discipline by the start date of the position Experience with mechanical modeling and simulation Experience in computer programming/scripting (e.g., C++, Python, Matlab
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learning and data science professional who wants to make a real-world impact? Do you have a specialisation in machine learning and affinity with project management? Do you want to apply advanced AI solutions
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for entry into a PhD program. A background in machine learning, inverse problems, scientific computing, or related data-driven methods is highly desirable. You are curious about combining physical modeling
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WDM switches and the fast control to enable novel low latency highly scalable and flat interconnect AI compute clusters. Machine learning clusters and artificial intelligence (AI) training have become
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and analysis of dedicated algorithms for training analog circuits directly from data. In this PhD project, you will develop a novel system-theoretic framework for learning in analog circuits and
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compositionally complex recycled steels, using density functional theory and machine-learned interatomic potentials, in close collaboration with leading academic partners and Tata Steel. Job description At TU Delft
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PhD, you will: perform large-scale potato experiments using 100 contrasting soil microbiomes; use automated high-throughput phenotyping to quantify plant growth, physiology and disease development
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reliable hydrogen-resistant circular steels. In this role, you will develop fundamental insights into the mechanisms governing hydrogen-induced degradation and failure of circular steels. As a PhD researcher