University of Wisconsin–Madison

Jobs at UW

University of Wisconsin–Madison



Apply now Job no: 93424-AS
Work type: Staff-Full Time
Location: Madison
Categories: Agricultural, Animal, Biological and Life Sciences, Engineering, Natural Resources, Environmental Sciences, Plant Sciences, Research, Scientific

Position Vacancy ID:


Employment Class:

Academic Staff-Terminal

Working Title:


Official Title:


Hiring Department:




Anticipated Begin Date:

MARCH 19, 2018


This position will end on MARCH 18, 2019.

Advertised Salary:

Minimum $48,000 ANNUAL (12 months)
Depending on Qualifications

Degree and area of specialization:

Ph.D. in Mechanical Engineering or highly related field

Minimum number of years and type of relevant work experience:

(1) Demonstrated Ph.D.-level expertise in applying machine learning, computer vision, image processing and data science to image-based metrological systems in the context of biological and material science research.
(2) One year or more experience developing machine vision/data analytics pipelines in an academic setting.
(3) Scholarly work products demonstrating strong programming skills in Python/C++/C/Matlab.
(4) Experience with modern deep learning frameworks and popular open-source computer vision libraries.
(5 ) Knowledge of wood structure and experience using machine learning to analyze images of wood anatomy and infer signal and features in wood using these techniques.

Additional competitive skills:

(1) Prior experience in developing inter-disciplinary measurement and data analysis systems.
(2) Prior experience in handling large datasets (e.g. images, genomic)
(3) Prior experience developing image processing/machine learning techniques for different imaging modalities.
(4) Experience working with hyper-spectral data.

License or Certificate:


Position Summary:

This position will work within the Department of Botany and in collaboration with the Center for Wood Anatomy Research at Forest Products Lab to identify research problems and design research methodologies to further the use of machine vision and machine learning to solve field wood identification questions in the context of combating illegal logging. This position will also Identify and develop machine-vision-based solutions to biological data extraction from standardized wood images for forensics and biology.

Additional Information:

This position will be for one year from the starting date. This position may be extended or become renewable if additional funding is obtained. Extension beyond that date would be contingent upon securing additional funding.


Julie Olson Paul
Relay Access (WTRS): 7-1-1 (out-of-state: TTY: 800.947.3529, STS: 800.833.7637) and above Phone number (See RELAY_SERVICE for further information. )

Instructions to Applicants:

Please click on the Apply Now button to start the application process.

An applicant may be hired in to an Assistant Scientist, Associate Scientist or Senior Scientist title, dependent upon experience. Title will be determined upon hire.

For questions on the position contact: Alex Wiedenhoeft at or at 608-231-9384.

To apply for this position, you will need to upload a cover letter, resume, and three professional references, including your current supervisor. References will not be contacted without advanced notice.

Your cover letter should address your qualifications as they pertain to the minimum number of years and type of relevant work experience listed above.

Additional Link: Full Position Details
  NOTE: A Period of Evaluation will be Required
  The University of Wisconsin is an Equal Opportunity and Affirmative Action Employer.

The Annual Security and Fire Safety Report contains current campus safety and disciplinary policies, crime statistics for the previous 3 calendar years, and on-campus student housing fire safety policies and fire statistics for the previous 3 calendar years. UW-Madison will provide a paper copy upon request; please contact the University of Wisconsin Police Department.

Applications Open: Central Standard Time
Applications Close: Central Standard Time

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