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. Familiarity with standard design verification (DV) procedures and continuous integration (CI) setups would be beneficial. Knowledge of machine learning workloads and the design of machine-learning accelerators
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of state voting legislation for the Voting Laws Roundup. This work includes developing computational tools (e.g., using large language models, machine learning for text analysis and classification, etc
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are encouraged to Be Curious about opportunities for learning, creating, discovering, and innovating, and are encouraged to learn from failure. Show Your Fire by joining our team and exhibiting your passion and
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. About You The successful applicant will have, or soon obtain, a PhD degree in mathematics or related, or equivalent level of professional qualifications and experience, with expertise in at least one of
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neurodegenerative diseases. The position is open to candidates with PhD degree and previous postdoctoral experience and offers the opportunity to work in an excellent team engaged in mouse models of disease
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, emissions, and productivity. Decision-Support & MCDA Implement a machine-learning-driven multi-criteria decision analysis to rank and select optimal decarbonization pathways. Collaborate with industry and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 14 hours ago
of patient care, education, and research. We strive to promote faculty, staff, and learner development in a diverse, respectful environment where our colleagues demonstrate professionalism, enhance learning
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biological robustness to join our team. Required: You possess a PhD or are close to completing your PhD in cell biology, developmental biology, stem cell biology, or a closely related field. You have strong
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Two fully-funded postdoctoral or PhD student positions in experimental mesoscale bioinspired materials, DNA nanoscience, and/or soft matter physics The university: Johannes Gutenberg University
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the disparities. While foundation models offer great promise for creating more robust machine learning models for a wide array of tasks, it remains an open problem how to foresee their biases across that wide array