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Vision conferences, CVPR, ICCV, ECCV and peer reviewed journals. Minimum Qualifications: PhD in Computer Science or a related field obtained within the last five years. Strong skills in machine learning
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graduate students in disciplines relevant to chemical risk assessment (e.g., toxicology, chemistry, endocrinology, AI/machine learning) and governmental staff presently involved in chemical risk assessment
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the collection, processing, and analysis of physiological data from recreational and elite athletes across various exercise protocols. Utilizing machine learning and deep learning algorithms, integrate multi-modal
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recovery. Oversee the collection, processing, and analysis of physiological data from recreational and elite athletes across various exercise protocols. Utilizing machine learning and deep learning
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and graduate students in disciplines relevant to chemical risk assessment (e.g., toxicology, chemistry, endocrinology, AI/machine learning) and governmental staff presently involved in chemical risk
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of this position may include: Single-cell RNA-seq, perturb-seq, and/or other transcriptomic analysis Next generation sequencing and bioinformatics analysis Machine learning/AI Analyzing data and presenting the data
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regression analysis or machine learning Drive and enthusiasm to both lead and work as a team member with a collaborative group. Effective oral and written communication, analytical and interpersonal skills
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, with an emphasis on microbiome and metagenome analysis. R and/or Python skills. Experience with statistical analyses, including logistic regression analysis or machine learning Drive and enthusiasm
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analyses, including logistic regression analysis or machine learning Drive and enthusiasm to both lead and work as a team member with a collaborative group. Effective oral and written communication