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), machine learning algorithms, and perception methods can improve the autonomy, robustness, and precision of robotic manipulation in challenging environments. The project lies at the intersection of control
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methods for additive manufacturing applications, including ANNs and evolutionary genetic algorithms for process optimization supported by programming knowledge (e.g. Matlab, Python). Hands-on experience
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green energy systems design, modelling, analysing the inertia impact on the next-gen transmission and distribution grid network Expertise in EMT & RMS Modelling tools such as PSCAD, DigiSilent
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measurements and collaboration with related valve disease projects. • IDIBAPS, Spain (2 months): access to clinical datasets and validation of developed algorithms against clinical and in-silico data. About You
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technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data
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at the interface of optimization, geometry, and machine learning Designing, implementing, and testing algorithms Engaging in scientific exchange with collaboration partners of the project Preparing reports
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-mentor relationship Prompt disclosure to the mentor regarding the possession and desire to distribute materials, reagents, software, and copyrightable and potentially patentable discoveries derived from
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Primary Duties & Responsibilities: Assists research studies with implementation of: existing algorithms and computer software for analyzing omics-based data sets [high-throughput, massively parallel genomic
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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National Lab, University of Tokyo etc.), the PhD candidate is expected to research on some of the following themes: New algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient