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Field
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multidisciplinary programme aiming to improve tools in, and knowledge from, genetics, genomics, molecular and single cell biology, spatial imaging, machine learning and novel methods of data handling to study the
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researchers from oncology, radiobiology, computer science, genetics, statistics, and basic/translational clinical research. Our research couples state-of-the-art computational and experimental techniques
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 months ago
. Duties/Responsibilities Analysis (50%) Develop machine learning algorithms to analyze ground magnetic field perturbations Analyze the results using machine learning interpretability techniques
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Scheduled Hours 40 Position Summary Performs innovative data analyses on neuroimaging and related data. Develop and implement computer algorithms for analyzing that data. Assume major responsibility
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& Responsibilities: Designs, develops, and implements: Algorithms and computer software for omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical.] Data management and analysis
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instructors posting on the university careers website. Application materials should be addressed to the faculty noted on the job description or to the chair of the program that you are applying to in cases
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, transcriptome sequencing, methylation assays, metabolomics, and/or lipidomics assays. State-of-the-art computational algorithms and new methods will be applied and developed to prioritize the variants and genes
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Description Primary Duties & Responsibilities: Implements: Algorithms and computer software for analyzing omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical]; Data management
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on developing advanced new algorithms, testing and validation, and applications in medical neuroimaging and non-imaging modalities. The candidate will contribute to the overall research goals and objectives
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), to start a research career in an environment committed to training outstanding cancer research scientists of the future. The Institute's particular strengths are in genomics, computational biology and