Biomedical Engineering and Engineering Science and Mechanics Faculty for Data Sciences/Machine

Biomedical Engineering and Engineering Science and Mechanics Faculty for Data Sciences/Machine Learning Applied to Biological/Health Sciences

The Departments of Biomedical Engineering (BME) and Engineering Science and Mechanics (ESM) seek applicants for a tenure-line/tenured faculty position with expertise at the forefront of development of Data Sciences or Machine Learning with particular emphasis on applications to Biological or Health Sciences.

This position is a co-hire between BME (bme.psu.edu) and ESM (esm.psu.edu). Both departments foster a highly interdisciplinary environment, promoting collaborations across the engineering disciplines, materials sciences, mechanics, chemistry, physics, mathematics, and biological sciences, and both combine traditional engineering principles with medicine and technology for the betterment of human health and society. This position is at the Assistant Professor level, but higher ranks will be considered.

Research Expectations: The successful candidates will have demonstrated expertise developing modern machine learning and data sciences methods and successfully applying them to substantially advance biological or health sciences. Research synergy with faculty in both departments, will be viewed positively, especially in neural engineering and neural data sciences, cardiovascular and/or cancer related research, biomechanics, biomimetics and mechanobiology; as well as personalized health and wearable technologies. Example topics of interest include but are not limited to: development of novel modeling and data analysis methods for large-scale multimodal neuro-electrophysiological data; advanced acquisition and processing in neural prosthetics; modeling of brain networks; identifying and optimizing treatments for neural, cardiovascular or cancer related diseases including pharmaceutical interventions, understanding and validating complex physiological and pathophysiological phenomena; and precision personalized medicine using electronic health records.

Teaching Expectations: The successful candidate will be expected to support the educational efforts in ESM and BME, to develop coursework targeted to enhancing data sciences training in our engineering education and enhancing data sciences practice in our research.

Diversity, Equity and Inclusion (DEI) Expectations: Inclusion, equity, and diversity are central to Penn State's obligation and commitment as a public institution of higher education to provide effective teaching for all people in our communities. The successful candidate will have a demonstrated commitment to enhancing diversity and will be expected to support the efforts in ESM and BME in advancing the University's DEI efforts (https://strategicplan.psu.edu/plan/foundations/inclusion-equity-diversity/).

Institutes and Centers: Cross-disciplinary and cross departmental collaborations are encouraged at PSU and are facilitated through a range of institutes and research centers. This position is envisioned to leverage particularly the Huck Institutes of the Life Sciences (Huck.psu.edu), the Institute for Computational and Data Sciences (icds.psu.edu), Materials Research Institute (MRI.psu.edu), College of Medicine, and the Center for Neural Engineering (cne.psu.edu).

Penn State College of Engineering: Penn State's College of Engineering is committed to creating a diverse and welcoming community that is pursuing engineering education, research, and community engagement with the power to inspire change and impact tomorrow. The college is also committed to work-life balance, a strong appreciation for diversity and inclusion, and family- friendly programs for faculty. We seek faculty candidates who share our vision and want to make an impact with their careers.

Qualifications: Required qualifications include a Ph.D. in an engineering science or biomedical engineering related discipline by the hire date, and a strong track record of accomplishments in research and teaching commensurate with rank of the candidate defined by PSU policy AC21 (https://policy.psu.edu/policies/ac21#D). In particular, applicants at Associate Professor should have well established teaching and independent research lines and international recognition. Applicants at the full Professor level should have a more substantial advanced research portfolio and will be expected to take leadership roles in research at the university. Nominations and applications will be screened immediately and considered until the position is filled.

Application Process: Applicants should submit, in one PDF file: (1) a cover letter summarizing impact in advancing data sciences/machine learning methods, and impact in a biological/health sciences, as well as synergies within the ESM and/or BME departments and ways the candidate would leverage/contribute within ICDS, (2) curriculum vitae, (3) statements of demonstrated contributions and plans for (a) research, (b) teaching, and (c) diversity, equity, and inclusion, (4) three relevant publications, and (5) names and addresses of four references; to job https://psu.wd1.myworkdayjobs.com/PSU_Academic/job/University-Park-Campus/Biomedical-Engineering-and-Engineering-Science-and-Mechanics-Faculty-for-Data-Sciences-Machine-Learning-Applied-to-Biological-Health-Sciences_REQ_0000020269-1.

Application review will begin immediately and will continue until the position is filled. The expected start date is August 15, 2022.

Inquiries: Inquiries can be directed to either of the search co-chairs Dr. Bruce Gluckman or Dr. Nanyin Zhang, ESM_BME_DataSciencesSearch@psu.edu.


Apply online at https://psu.wd1.myworkdayjobs.com/PSU_Academic/job/University-Park-Campus/Biomedical-Engineering-and-Engineering-Science-and-Mechanics-Faculty-for-Data-Sciences-Machine-Learning-Applied-to-Biological-Health-Sciences_REQ_0000020269-1

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Penn State is an equal opportunity, affirmative action employer, and is committed to providing employment opportunities to all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.

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