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JOB TITLE
Remote Adjunct Teaching Professor – Power Systems Engineering

LOCATION
Worcester

DEPARTMENT NAME
Graduate Studies – Instructors – JM

DIVISION NAME
Worcester Polytechnic Institute – WPI

JOB DESCRIPTION SUMMARY
Worcester Polytechnic Institute is seeking part-time adjunct faculty to teach graduate courses in Power Systems Engineering in an online/asynchronous delivery format. Schedule may vary, depending on need. Start dates will vary by course and semester.

This is a 100% remote position. Although Power Systems Engineering courses are delivered online, all applicants must be US-based.

JOB DESCRIPTION

Adjunct Teaching Professor will teach Power Systems Engineering graduate courses, primarily in an online, asynchronous delivery method. All applicants must be US-based.

Specific courses needed from WPI’s curriculum in Power Systems Engineering: https://www.wpi.edu/academics/online/study/power-systems-engineering-meng.

Ideal applicant will hold an advanced degree, preferably a PhD, in Electrical Engineering/Power Systems Engineering or related discipline with significant practical experience in the relevant area. Successful graduate-level teaching experience is preferred.

Required Documents:

Cover Letter Resume

Optional

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With multiple grants and research infrastructure provided by the U.S. National Science Foundation, researchers have shown that a newly developed material, niobium phosphide, can conduct electricity better than copper in films that are only a few atoms thick. These films can also be created and deposited at sufficiently low temperatures for compatibility with modern computer chip fabrication — and may help make future electronics more powerful and energy efficient.

So far, the best conductor candidates to outperform copper in nanoelectronics have had only exact crystalline structures, meaning they require very high temperatures to be formed. These new niobium phosphide films are the first examples of noncrystalline materials that become better conductors as they get thinner. The research is led by Standford University and results were published in Science.

“We are breaking a fundamental bottleneck of traditional materials like copper,” says Asir Intisar Khan, a postdoctoral researcher at Stanford University and an author on the research paper. “Our niobium phosphide conductors show that it’s possible to send faster, more efficient signals through ultrathin wires. This could improve the energy efficiency of future chips, and even small gains add up when many chips are used, such as in the massive data centers that store and process information today.”

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Implementation Update: Promoting Maximal Transparency Under the NIH Guidelines for Research Involving Recombinant or Synthetic Nucleic Acid Molecules

Institutional Biosafety Committees (IBCs) serve as a critical linchpin in ensuring the safe and responsible conduct of research. Since the issuance of the NIH Guidelines for Research Involving Recombinant or Synthetic Nucleic Acid Molecules (NIH Guidelines) in 1976, IBCs have expanded in number to the thousands and have voluntarily expanded their roles to encompass new research approaches as they arise.

IBCs continue to serve as a pillar of biosafety oversight and are essential in building trust on behalf of the biomedical research enterprise. Under the NIH Guidelines, this expectation is a mandate and as such, NIH is reinforcing its commitment to working with IBCs to ensure transparency in biosafety oversight by updating its implementation expectations to protect the safety of all

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Protecting Human Genomic Data when Developing Generative Artificial Intelligence Tools and Applications

Artificial intelligence (AI) tools and applications are proving to be transformative for driving new biomedical research advances. While development and use of generative AI is becoming increasingly prevalent, NIH urges the research community to remain vigilant of potential risks of inadvertent data disclosure when sharing AI tools and applications. Specifically, NIH reminds researchers that:

The Genomic Data Sharing (GDS) Policy and the subsequent Data Use Certification (DUC) Agreement prohibit users from distributing controlled-access data (including genomic or associated data) or their Data Derivatives to any entity or individual not identified in their Data Access Request without appropriate written approvals from the NIH. Sharing, retaining, or training generative AI models using controlled-access human genomic data may risk disclosing controlled-access data and, thus, violates the Non-Transferability provision of

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