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) (required) - Quantum libraries (Cirq, Qiskit, Tensorflow Quantum. QuTiP) (recommended) Theory: - Machine Learning, Deep Learning, Reinforcement Learning, Data Science (required) - Quantum Computing, Quantum
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Instrument for Magnetic Sounding (PIMS) on the Europa Clipper Mission. Space Sci Rev 219, 62 (2023). https://doi.org/10.1007/s11214-023-01002-9 3. Kataoka, R., Nakano, S. & Fujita, S. Machine learning emulator
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to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust, interpretable models from experimental and operational data
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 9 days ago
dynamics data and advanced graph-based deep learning models to decode long-range communication pathways within macromolecular complexes. The PhD candidate will play a central role in this effort by
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involve developing an approach that uses Knowledge Organization (KO) metadata and ontologies to optimize parallel processing and scheduling policies (via Kubernetes) for Machine Learning tasks. The fellow
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outcomes. Key Responsibilities Develop, implement, and optimise AI/ML models (artificial intelligence/classical machine learning, deep learning, computer vision, NLP, etc.) Work with structured and
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physics-informed machine-learning models for binding affinity predictions in rational small-molecule drug design. The models will allow prioritisation of candidates from hit discovery through to lead
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and assist with data analysis, including mathematical modeling and/or machine learning: keep accurate records of experiments and results perform data interpretation/summarization including writing
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analyses. Machine learning for biological data (e.g., protein language models, transformers, generative models) and interest in building interpretable tools for experimental colleagues. Qualifications PhD
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Architecture Search (NAS) that can automatically design efficient deep learning models optimized for specific embedded hardware platforms. These models will be deployed in resource-constrained, standalone