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Field
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journals. FTE: 0.25 Work Performed Research (70%) • Lead execution of near-term quantum algorithms on trapped ion quantum computers • Develop experiments and provide support for ion trap quantum, computation
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detailed documentation of AI models, algorithms, and system architecture; ensure compliance with data privacy and security standards. Perform all other duties as assigned by the Associate Dean
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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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: Strong programming skills in Python (NumPy, pandas, PyTorch or TensorFlow) Machine learning fundamentals and algorithmic thinking Ability to design and implement computational experiments Clear technical
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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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such as text, tabular, genetic, environmental, wearable, or image data. Strong publication record in medical and/or biomedical informatics related journals and conferences Preferred Qualifications
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and optimize advanced signal processing algorithms for joint communication and sensing. Analyze experimental datasets, extract statistical models, and compare findings across environments, hardware, and
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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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of complex and large-scale genomics data, including (but not limited to) WGS and transcriptomic data (including single-cell RNA-seq and spatial transcriptomics data). Use computational tools and algorithms
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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