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Posting Summary Logo Posting Number RTF00176PO26 USC Market Title Post Doctoral Fellow Link to USC Market Title https://uscjobs.sc.edu/titles/156387 Business Title (Internal Title) Post Doctoral
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engineering and clinical physiology. Projects may involve signal quality assessment, artifact detection, waveform segmentation, feature extraction, hemodynamic modeling, time-series analysis, machine learning
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robotic middleware (e.g., ROS, MoveIt) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic
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control, network optimization, or joint communication and sensing is highly desirable. Experience with artificial intelligence, machine learning, reinforcement learning, or optimization techniques applied
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strands. • Bespoke modelling of tumour metabolic function using 3D and 4D imaging data • Cancer patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics
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learning, understand their mathematical foundations, and connect them to space-related technologies and missions. The focus is on building rigorous models that explain and predict the behaviour of modern
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substantial intellectual independence and opportunities to collaborate closely with machine learning scientists developing predictive models of cellular function. Machine learning experience is not required
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Science Research unit: https://genomics.iit.it/ ESSENTIAL REQUIREMENTS PhD in computational biology, machine learning, bioinformatics, physics or related fields; High proficiency level in programming
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Scholar to develop and pursue independent projects during their training. The Beach and Oakes labs broadly investigate how cells generate and interpret mechanical signals using cell culture models and
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models