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bioinformatics analysis pipelines for processing RNA-seq, single-cell RNA-seq, genomics and proteomics data. Develop novel algorithms and integrated data visualization applications when existing software packages
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that includes medical image processing, Artificial Intelligence (AI) algorithm development, and scientific writing. Gain clinical experience including training in radiation oncology software, imaging including
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differentially private learning, its connections to replicability of algorithms, and algorithmic fairness. Basic Qualifications Candidates are required to have a doctorate or terminal degree in Computer Science
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RNA-seq, genomics and proteomics data. Develop novel algorithms and integrated data visualization applications when existing software packages are not available or are not adequate. 2.) Apply
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | 2 months ago
algorithm performance. You will analyze assignment data from December 2025 to the present to identify workload distribution patterns and refine the algorithm using mathematical modeling, programming, and
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. Utilize machine-learning and data-mining approaches to recommend bioengineering interventions. Develop new machine-learning algorithms. Integrate machine learning techniques with mechanistic modeling
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of detailed protocols including operational definitions, data dictionaries and ICD/CPT codes used for algorithms; will be responsible for making statistical analyses code publicly available on GitHub ensuring
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by applying existing and novel computational biology, bioinformatic, and machine learning algorithms to sequencing datasets and correlating them with multi-dimensional clinical datasets that contain
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settings. Develop and test algorithms for object detection, tracking, and classification using wireless sensors. Help guide and mentor graduate students and other junior team members working on the project
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of artificial intelligence systems. Relevant areas may include, but are not limited to, algorithmic fairness and bias, transparency and accountability of AI systems, AI safety and alignment, privacy and data