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Field
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Status: Nonexempt Work Schedule: Flexible Summary The intern will receive training on machine learning algorithms, implementing them, and fitting them on the datasets in our cluster computers. Overall
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chemical reactions and assembling functional nanomaterials from bottom-up in scanning tunnelling microscopy (STM). The project is tightly linked to machine learning algorithms in images (image classifier
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interaction and analytics using AI. Key Responsibilities: Development of artificial intelligence (AI) technologies to perform human-robot interaction and analytics System integration of the developed algorithms
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variants of importance sampling. We will connect these methods to modern formulations of Monte Carlo algorithms to improve their accuracy, scalability, and overall computational cost. The methodology so
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algorithms proficiency in both general-purpose programming (e.g., Python) and scientific computing, with a preference for experience in Julia Experience in writing up research work for publication Highly
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nanocrystal morphology and optical property characteristics. The researcher will work collaboratively with graduate student(s) to design and implement a machine learning (ML) algorithm using the database as
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of scientific data, e.g. from image acquisition modalities or scientific simulations. Efficient algorithms are at the core of most of these data analysis and visualization applications. The focus of this Ph.D
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collection methods for external data (e.g. web scraping) to collect data for use in modelling and analysis. Develop analytic plans, engineer supporting algorithms, write new functions or applications in
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learning including reinforcement learning, nature inspired algorithms, deep learning, agent based modeling, and natural language processing. We are specifically interested in research focused
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candidates whose work lies at the intersection of statistics, machine learning, data analytics and modern AI algorithms. This includes, in particular, statistics for high-dimensional and complex data