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Field
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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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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
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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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, and ionization cross sections using data derived from experiment and computation. - Implementing algorithms to study the performance of AI/ML classification models. - Assessing
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genetic knockouts in yeast and mammalian cell lines, and protein purification. Job Responsibilities: 35%: Computational algorithm development and data analysis 35%: Design and conduct experiments with yeast
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Requisition Id 15000 Overview: We are seeking an Associate R&D Staff member who will focus on algorithmic tools for process monitoring, diagnosis, and control. This position resides in the Sensors
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econometric analysis and preparing results tables, managing large data sets, handling spatial data, applying machine learning algorithms, conducting computationally intensive statistical analyses, summarizing
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for an Artificial Intelligence and Machine Learning (AI/ML) scientist to develop advanced computer vision workflows to support the identification of biomolecules in cryo-electron tomography (cryo-ET) image volumes