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Field
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, and finally using deep learning to solve the complexity challenge associated with coherent beam combination. The role Within HiPPo, your specific task will be to develop a ‘digital fibre laser’, through
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Sensing (DAS) data processing and compression using ML Physics-driven machine learning for geophysical modeling and inversion Thus, the candidate is expected to have or about to have a PhD in a relevant
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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates
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research, formulate hypotheses, and design effective experimental plans. Strong programming skills with deep learning frameworks (e.g., PyTorch). We regret that only shortlisted candidates will be notified
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including functional enrichment (GO, KEGG), network analysis, genome assembly and binning, systems biology, and multi-omics integration. Apply statistical modelling, machine learning, and deep learning
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, and who are eager to contribute to impactful methods for generating private and fair synthetic data with good utility. This project involves development of deep learning based synthetic data generators
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The candidate will be expected to work on a project in collaboration with Schaeffler to conduct research on “collision monitoring and control system of cobot based on fiber optic sensing and deep
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required. Substantial experience in machine learning, Python and R programming, and familiarity with deep learning packages (e.g., TensorFlow, Keras, or PyTorch) are essential. Additional Qualifications
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for scent signals. Prior research experience and track record in signal detection, machine learning and deep learning. Prior programming experience in state-of-the-art AI techniques. Mastering of a
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requirements PhD in Physics, Applied Mathematics, Computational Science, or a related field Strong background in machine learning, particularly in the development and application of neural networks