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sensor integration. Strong coding and debugging skills. Excellent communication, documentation capabilities and a demonstrated track record of publication. An enthusiasm for developing new measurements
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support. Preferred Knowledge, Skills, and Abilities: Ability to program and debug python code, particularly Jupyter notebooks. Experience in working on collaborative software projects. Environmental, Health
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preparation. Prepare grant proposals based on conducted research and findings. Understand and adhere to Rutgers’ compliance standards as they appear in RBHS’s Corporate Compliance Policy, Code of Conduct and
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of Researchers (Charter and Code). Please, check out our Recruitment Policy The role We are seeking a motivated postdoctoral scientist interested in dissecting genome regulatory mechanisms underlying human
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cardiovascular treatments. In this role you will apply both machine learning predictive modelling and human genetic analyses of non-coding regulatory sequences to identify cell-specific targets for coronary artery
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of the research group and others as described in the Duke University School of Medicine Honor Code of Professional Conduct and other relevant conduct policies about other schools at Duke University. Compliance with
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genomic approaches Application of the modeling approaches in relevant downstream tasks Co-development of high-performance computing AI training codes for the first European Exascale Supercomputer JUPITER
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a related field Experience with radiation transport codes (e.g., FLUKA, Geant4, MCNP etc.) Excellent programming and data analysis skills (e.g., Python, C++, or similar) Solid understanding
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, demonstrated experience of coding in programming languages such as R and Python is considered particularly advantageous. Examples of computationally intensive methods central to IAS and IDA are data-driven text
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preferred. Required Qualifications • Ph.D. in biosciences • English proficiency • Publication(s) in a peer-reviewed journal Job Code: 00003820 POSTDOCTORAL ASSOCIATE Job Level: 00 Duke is an Affirmative