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. Required Qualifications: A doctoral degree (PhD, MD, or equivalent) conferred by the start date. Proficiency in R/Python Experience with scRNAseq, and/or spatial proteomic/transcriptomic data analysis Growth
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, bioinformatics, molecular biology, toxicology, pharmacology). 2. Proficiency in bioinformatics, programming, and tools for the analysis of large genomic datasets and single-cell dataset, including R, linux, python
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. Expertise in computational neuroscience software (e.g., MATLAB, Python) as well as statistical methods and statistical packages (e.g. SAS, R). Experience with machine learning methods is preferred
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programming languages (e.g., Python, MATLAB, R) for complex data analysis Excellent written and verbal communication skills Ability to work both independently and collaboratively in a research setting Preferred
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Science. Proficiency in programming (Python, Julia), and high-performance computing (provide evidence with specific examples) Ability to work independently and collaboratively. Strong written and oral
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and other machine learning models (especially neural network models, time-series models) and coding in python and R. Strong collaborative skills and ability to work well in a complex, multidisciplinary
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applying data science principles and techniques to biomedical datasets. Applicants should be comfortable using coding skills such as SQL, R, and Python to extract data from databases, clean it, and analyze
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or MD/PhD Strong programming skills, preferably in R and/or Python Previous expertise and/or interest in single-cell sequencing technologies, bioinformatics, spatial analyses, and generative AI is desired
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Python and Julia with experience with deep learning frameworks (e.g., PyTorch, TensorFlow). Experience building complex software systems, preferably with industry experience. Research background in
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with electronic health record (EHR) and/or clinical data. Proficiency in Python, with strong coding and debugging skills. Experience with deep learning frameworks such as PyTorch, JAX, TensorFlow