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of bioacoustic signals. Extensive experience with programming (Matlab, R, Python) including GPU programming is required, and familiarity with edge-based machine learning (particularly sound event detection), open
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with diverse groups of students, staff, and faculty members. Proficiency in computer software and applications, such as Microsoft Office Suite and other relevant tools. Commitment to continuous learning
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development in support of MSD research programs • Learn about the underlying technical concepts of assigned programs • Participation in weekly program meetings and periodic sponsor meetings (requiring oral and
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analyzing machine learning experiments; supporting scaled experiments involving neural networks, large language models, and GPU clusters; improving reproducibility, reliability, and usability of research
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. Possible research topics include: Scientific machine learning Numerical partial differential equations (PDEs) Computational fluid dynamics Neural operators High-performance computing Data-driven
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Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 2 months ago
genetic data Multimodal machine learning for biological discovery Translational genomics and risk modeling The fellow will work in an environment that emphasizes methodological innovation, statistical rigor
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Engineering, Biomedical Engineering, Medical Imaging, Signal Processing, Applied Physics, Computer Engineering, or a closely related discipline. Strong background in at least one of the following: ultrasound
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. Individuals in this position will perform technical work supporting the collaboration's scientific research. Research Title: Data Scientist / Computer Scientist - Standardized AI-Ready Dataset Frameworks for AI
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Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI workflows for grid
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of mental health speech transcripts Design and extract text features for modelling ultra-high-risk outcomes Design and improve existing multimodal machine learning frameworks for predicting ultra-high-risk