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Field
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assist in developing quantum reservoir computing algorithms and work using them to model power systems data from, in particular, NLR’s ARIES platform, to explore the limits of quantum reservoir computing
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The Associate in Research will be responsible for using and developing computational algorithms to analyze single-cell and spatial-omics datasets. Specifically, we have multiple projects where we are generating
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the construction of PRS and enhance disease prediction. Students will gain experience in: Statistical genetics and GWAS methodology Machine learning approaches for high-dimensional data Algorithm development and
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Perrimon’s group in the Program of Genetics at Harvard Medical School. Perrimon lab is actively generating data sets of omics scale and this position will involve working with other post-doc trainees in
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Evolutionary algorithms or genetic programming Financial compliance, regulatory technology, or financial crime prevention domain knowledge Adversarial machine learning or AI security Personal attributes: Strong
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algorithms for spatial and single-cell omics, integrates publicly available datasets into a centralised data hub, and leverages this platform to build AI models for biomarker discovery and drug target
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, quantitative genetics, and large-scale omics data, etc. Develop next-generation breeding algorithms and decision-making engines for crops, livestock, poultry, and aquaculture applications. Create original AI
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Scientist to join our center. The ideal candidate will focus on the development and maintenance of advanced pulmonary MRI and CT image processing algorithms and AI solutions aimed at improving lung disease
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chromatography/mass spectrometry (LC-MS) measurements using principal component analysis, partial least squares, genetic algorithms, and other multivariate statistics. Current projects have accumulated a
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oversee and develop algorithms for analyzing ensemble genomics data, single cell genomics data, single cell merFISH and sequential oligopaints imaging data, as well as novel molecular connectomics data