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and algorithmic perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern
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development and evaluation. This opportunity will prepare candidates for a range of competitive positions in academia or industry that involve machine-learning for biological or chemical data, computational
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research Managing and analyzing data, implementing machine learning algorithms on data Conducting literature reviews Preparing presentations, manuscripts, and grant submissions Assisting with research
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the application of statistics and machine learning in social science. The position requires no teaching, though teaching opportunities may be provided if requested. When teaching, successful candidates will carry
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Qualifications: A PhD, MD/PhD, or equivalent research doctoral degree in neuroscience, biomedical data science, computer science, psychology, psychiatry, statistics, engineering, applied mathematics, or a related
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modern machine learning and a strong record of research accomplishment who are excited to build brain foundation models and other AI systems that advance our understanding of neural activity, brain
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analytics, including correlation analysis and machine learning techniques. Preferred Qualifications: Experience with microstructure characterization techniques (SEM, EBSD, TEM, XRD). Experience in mechanical
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, biophysics, applied mathematics, or related field. Expertise in machine learning, computer vision, or image analysis. Experience with data management and databases. Experience working with Python, scientific
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migraine and other headache disorders, neuroepidemiology, and other neurodegenerative disorders. We use advanced biostatistical modeling, artificial intelligence (AI), machine learning, and neuroinformatics
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the