« View All Jobs

Postdoctoral Research Data Scientist (15223B)

gphughes – Posted on September 11, 2017 at 3:35 pm –

  • Job Type: Post-Doc
  • Employer: University of Utah
  • Location: Salt Lake City, Utah
  • Link: https://www.utah.edu/
  • Job Descriptions:

The Scientific Computing Imaging Institute
(SCI) at the University of Utah conducts original research to establish the
scientific capabilities that govern the design, performance, and integration of
computational tools into scientific platforms. In this position, you will be an
employee of the University of Utah and have an appointment at Idaho National
Laboratory. As a member of the team that spans the University of Utah, Idaho
National Laboratory, and Oak Ridge National Laboratory you will provide
analytical and programming expertise in the use-inspired and application of
deep learning to support basic and applied research for Idaho National

As an experienced postgraduate researcher for
the applied optimization or machine learning you will ensure that
state-of-the-art methods are applied within a growing national laboratory and
university team to unlock the full potential of advanced microscopy and
materials characterization. You will run and lead projects in a wide variety of
applications to bring about scientific and technology benefits to our team by
challenging and collaborating with other scientists, professors, engineers, and
business professionals on the utility and application of novel image
reconstruction and analysis approaches, and spectral analysis approaches to
provide ground-breaking capabilities for material science applications. You
will work in interdisciplinary team comprised of scientists, engineers, and
business professionals to realize the full potential and utility of deep learning
for unlocking the potential revolutionary speed-ups to data access and
extraction of microscopy datasets.

For example, you will improve on the utility
and design of data science tools for microscopy that realize new paradigms in
data acquisition, information storage, and technique development using these
advanced tools and software packages. You will optimize the data science tools
and developments across several installations including national laboratories
and universities. Based on the development you will deploy trained models that
forecast and suggest alternatives to sampling materials data for realizing the
temporal, spatial, and spectral limits. Based on your developed and trained
models you will discuss and present these improvements with members of top
universities and national laboratories at professional conferences, meetings,
and high impact peer reviewed publications. 
With your expertise, you will also help to identify new application areas, use
cases, or technologies by applying your innovative data analytics methods and
technologies. As the team is a collaboration between Idaho National Laboratory
and the University of Utah, you will have the chance to actively involve
yourself in the field of advanced microscopy and data science at both institutions.

of Utah Job ID# PRN15223B, Scientific Computing & Imaging Institute (SCI

COMPENSATION: $70,000/year. Strong benefit package
including retirement and health plan options.

WORK SCHEDULE: Full-time, 40
hours/week, days.


• Work with
large, complex data sets. Solve difficult, non-routine analysis problems,
applying advanced analytical methods as needed. Conduct end-to-end analysis
that includes data gathering and requirements specification, processing,
analysis, ongoing deliverables, and presentations.

• Build and prototype analysis pipelines
iteratively to provide insights at scale. Develop comprehensive understanding
of microscopy data structures and metrics. Advocate for changes in data storage
and access file systems where needed.

• Develop and implement novel image
reconstruction and analysis approaches for microscopy using deep learning and
machine learning packages.

• Develop and implement novel spectral analysis
and approaches.

• Develop novel approaches for high-content

• Interact and work in an interdisciplinary
cross-functioning team to develop and validate software tools for material
research and microscopy applications. Work closely with scientists and
engineers to identify opportunities for, design, and assess improvements to
envisioned final products.



• PhD degree in
Physics, Computer Science, Electrical Engineering or related technical fields,
or equivalent practical experience

• 2 years of relevant work experience in data
analysis or related field. (e.g., as a statistician / data scientist / material
scientist/ physicist).

• Experience with programming software (e.g. R,
Python, C/C++, Linux, MATLAB, Labview) and
database languages (e.g., SQL). 

• Experience implementing image processing and
reconstruction algorithms.

• Experience with machine learning (Tensorflow,
Caffe2, PyTorch, MxNet)

• Proven experience in applying creative and
practical Data Science methodologies to extract, process and transform data
from multiple sources



• 4 years of
relevant work experience in data analysis or related field. (e.g., as a
statistician / data scientist / material scientist/ physicist), including deep
expertise and experience with statistical data analysis such as linear models,
multivariate analysis, stochastic models, sampling methods. Analytical
engagements outside class work while at school can be included.

• Applied experience with machine learning on
large datasets.

• Experience in mathematics and algorithms.
• Experience with material science and/or

• Good communication and collaboration skills.




The University of Utah is an Affirmative Action/Equal Opportunity employer.
Upon request, reasonable accommodations in the application process will be
provided to individuals with disabilities. Please contact the Office of Equal
Opportunity and Affirmative Action, 201 S. Presidents Cr., Rm 135, (801)
581-8365 (V/TDD), for further information or to request
an accommodation. The University of Utah is committed to diversity in its
workforce. Women and minorities are encouraged to apply.

k();} ?>