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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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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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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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Job description We invite applications for a fully funded PhD position in the area of Scientific Machine Learning (SciML), which integrates data-driven machine learning techniques with established
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at the intersection of computer vision, micro-electronics analysis, and hardware security, and will work under the supervision of researchers within the Department of Intelligent Systems. The PhD researcher will be
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The Data Management and Biometrics (DMB) group at the University of Twente is seeking one PhD candidate to join the research team of Dr. Nicola Strisciuglio to work on compositional learning