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assessment of chemical plants using HAZOP analysis Use of process modeling and simulation to enhance quantitative assessments Use of machine learning to support HAZOP discussions with the aim of obtaining a
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years of post-PhD research and engineering experience in AI for mobile security Solid knowledge in adversarial machine learning or trustworthy AI, including experience with robustness assessment and
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
properties (hardness, yield and tensile strength) and corrosion profile (rate and localization). This work focuses on machine learning-assisted PSPR optimization of recently developed lean Mg-0.1 Ca alloy
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machine learning approaches to quantitatively analyze experimental data and predict emergent multicellular behaviors under varying mechanical and chemical environments. For more information about our lab
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autonomous driving. Your profile Master's degree in Computer Science, Artificial Intelligence, Robotics, or related field Strong background in machine learning, deep learning, or computer vision Experience
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paraganglioma driven by cell plasticity using spatial transcriptomics and machine learning.” High-risk neuroblastoma (NB) and malignant paraganglioma (PPGL) are neural crest–derived tumors with pronounced
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machine learning or trustworthy AI, including experience with robustness assessment and attack/defense mechanisms. Expertise in software security and code analysis, with understanding of common
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, integrative biology approach that utilizes human pluripotent stem cell based model systems, high throughput functional genomic screening and big data based machine learning, bridging the scales from genetics
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analysis Background in biomedicine and digital pathology What we offer Embedding within a computational team, with extensive experience in computational biology and machine learning. Embedding within
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increasingly complex networks. By deploying and advancing techniques such as machine learning, graph-based network analysis, and synthetic data generation, the project tackles key challenges in anomaly detection