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workers, staff, and faculty to teach and support these workshops. 3. Assist with the development and support of funded DH scholarship, both at USM and at partner institutions across the state, including
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for the development and implementation of innovative statistical and machine learning approaches for analysis of genetics and genomics data. We are particularly interested in applicants who have experience with methods
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, skills and abilities: -Proficiency in image analysis using machine learning methods. Continued employment in this position is contingent upon availability of funds. Please attach the following to completed
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experience: Familiarity with machine learning hardware Familiar with high performance scientific infrastructure Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term
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. • Develop computational and theoretical models that bridge neural data and behaviour, leveraging modern machine‑learning toolkits. • Drive multi‑lab collaborations across SCENE; co‑author high‑impact
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data set to be used for Machine Learning (ML). They will validate a predictive model generated by ML by preparing new samples with ideal monomers and contrasting their properties with predicted values
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manuscripts, present oral presentations locally and nationally, and learn other skills necessary for a successful career in cardiovascular research. The postdoc, if eligible, is strongly encouraged to apply
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manipulation tools (e.g., CRISPR/Cas9) is a plus. Other duties as assigned. Minimum Education and/or Work Experience PhD required Qualifications PhD Certificates/Credentials/Licenses PhD Computer Skills Office
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analysis of atmospheric numerical model output (e.g., WRF, PALM, SAM) Experience with machine learning and artificial intelligence techniques Experience with predictive modeling Environmental, Health
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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