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guarantees. This includes working with techniques such as differential privacy and PAC-privacy to enable safe model and explanation release. Familiarity with privacy-preserving machine learning methods is a
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physics and permeability evolution models from µCT data using machine learning and computational tools (PuMA/CHFEM/MOOSE) validated against experimental observations Bridging scales from pore-level
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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stimulating, interdisciplinary environment. develop and validate machine learning models to extract digital biomarkers for atypical parkinsonism from real-world wearable sensor data. interpret findings in close
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). PhD students in our department receive excellent training and ample opportunities for feedback. In addition to standard required course work, students typically take courses in machine learning, (micro
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mass spectrometry based de novo sequencing, machine learning and AI-tools to interpret the data. Your job The primary objective of the project is to further develop mass spectrometry-based techniques
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repaired, reused, or discarded requires sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models
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creation that controls clogging patterns Developing predictive digital rock physics and permeability evolution models from µCT data using machine learning and computational tools (PuMA/CHFEM/MOOSE) validated
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predictive digital rock physics and permeability evolution models from µCT data using machine learning and computational tools (PuMA/CHFEM/MOOSE) validated against experimental observations Bridging scales
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by the Fondation Paralysie Cérébrale—you will help develop a machine learning-based multimodal prediction tool for CP diagnosis and long-term outcomes. This tool will integrate advanced clinical