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Field
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: Bachelor’s degree in a biological science, health science, or related field, or equivalent combination of education and relevant experience. Proficiency in digital and computational pathology, AI/ML algorithms
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comparison of predictive algorithms—such as Random Forest and LSTM neural networks—for use in genetic material recommendation systems. Working as part of Camcore’s data science team, the selected candidate
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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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grid analytics, and scientific imaging. The successful candidate will design and implement sparse algorithms for large-scale scientific and numerical computations. This role offers an exceptional
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Driven Discovery. Job Responsibilities: Analyze biomedical data with minimal supervision by performing advanced analysis, algorithm implementation, programming, and quality check. Assist senior analysts
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. Experience: Experience in a research environment is highly preferred. Background and work knowledge in algorithms, scientific computing, and machine learning or statistics is preferred. Proficiency with C/C
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The successful candidate will be responsible for developing computational and systems biology approaches to analyze spatial omics data and single-cell omics data (e.g., scRNA-seq, scATAC-seq, single
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systems, multi-function radar, AI/ML algorithms rely on high performance digital signal processing and real-time computing to provide high-fidelity results in an actionable timeframe. The ARRC intends
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theoretical mathematical foundations and practical implementation of circuits and computer algorithms. The course presents applications in engineering, physics, feedback and control, communications, and signal
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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