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Field
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biology or related field Experience with omics data analysis (e.g. transcriptomics, metabolomics) Knowledge of machine learning/deep learning algorithms Knowledge of systems biology approaches (e.g. genome
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Pharmaceutical health outcomes, (Pharmaco)epidemiology, Biostatistics, or a related field. · Expertise in or a strong interest in machine learning and deep learning algorithms. · Excellent communication skills
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optimizing these patterns based on the Gerchberg-Saxton algorithm, and then building out an optical setup to test whether these phase patterns can successfully turn a Gaussian beam into the arbitrary predicted
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, and language modeling. Students will acquire hands-on experience by implementing models such as clustering algorithms, automatic text categorization, and experimental evaluation. As an introduction
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platforms including 454, Illumina, and Pacific Biosciences. Manages and redefines data to the necessary formats. Helps to identify, evaluate, and incorporate relevant algorithms and software pipelines by
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and implement power grid planning/operations algorithms and tools and conducts data analysis related to energy and power systems, with emphasis on the following areas: Variable energy resource
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dynamics (algorithms and current practice). Experience in AI/ML. Experience in application performance optimization. Familiarity with parallel programming models (e.g., OpenMP, MPI, CUDA). Experience
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the horse racing and equestrian industries. • Track social platform updates and algorithm shifts to optimize content performance. • Manage digital assets and relationships with in-kind sponsors and brand
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collected and writing up experiment results in the field of computational biology. Core responsibilities include: Working with biological data - implementing algorithms, programming and data analysis
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research applications based on knowledge of the theoretical foundations of information and computation including algorithms and data structures, and the application of state-of-the-art programming