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and skill in programming with MATLAB or Python. Research experience in MEG, in vivo electrophysiology, in vivo two-photon/miniscope imaging, slice electrophysiology, and mouse brain surgery is desired
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. Required competencies: Strong background in bioinformatics (e.g., R, Linux, Python). Experience working with large cohorts and high-dimensional data. Experience with microbiome analysis and/or GWAS
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RAS signaling and/or targeting is preferred Expertise in computational biology/bioinformatics (including proficiency in Python/R) would be desirable, though this skill is not essential Prior experience
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learning, or a related field Strong background in deep learning and statistical analysis Proficiency in Python, R, and deep learning frameworks (e.g. PyTorch, TensorFlow) Strong written and verbal
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to process tissue data reproducibly and at scale Conduct analyses using programming languages such as R and Python Collaborate with other laboratory members with expertise in epidemiology, bioinformatics
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Computer Science, Biomedical Engineering, Statistics, Biomedical Sciences, Electrical Engineering, or a related quantitative field Strong programming skills (e.g., Python, R, or similar) Demonstrated research
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applicant should have a proven track record of publications, have previous experience with genomics data analysis, be fluent in at least one of the following programming languages: C++, Python or R, and will
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field that provides a strong research background for the project. Fluency in English and Python are required. Research experience working with large-scale machine learning projects, extensive research
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technologies, is highly desirable. Skills: Strong knowledge of clinical informatics frameworks, standards, and methodologies. Proficiency in data analysis software (e.g., R, Python, SAS, SPSS, SQL) and
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, integration, clustering, and annotation ● Proficiency in Python and/or R for large-scale data analysis ● Experience developing reproducible workflows, pipelines, and scalable data-processing frameworks