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Eligibility requirements for the position. Required Qualification: Ph.D. in bioinformatics, computational biology, statistics, computer science, or related field Proficiency in R or Python Minimum one year of
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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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compositionality with large language models. * Strong Python and ML ecosystem skills (e.g., PyTorch, scikit-learn, etc.). * Facility with C/C++/Rust or other systems-level programming languages * Skilled in writing
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reporting standards * Familiarity with deep learning technologies (e.g., CNNs, transformers) * Experience with Python and ML ecosystem skills (e.g., PyTorch, scikit-learn, etc.). * Skilled in writing and able
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also being a mentor to other members of the community. KNOWLEDGE, SKILLS, AND ABILITIES REQUIRED: Background in machine learning Background in cancer biology Experience with R and/or Python Experience
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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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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