[ccp4bb] Postdoctoral position in molecular image analysis methods development, University of Nebraska

[Mark Wilson <mwilson13@UNL.EDU>]

Dear Colleagues,
There is a postdoctoral position at the University of Nebraska in methods
development for molecular image analysis. Application review has started
and will remain open until the position is filled. Please see details
below.
Best regards,
Mark

Mark A. Wilson
Associate Professor
Department of Biochemistry/Redox Biology Center
University of Nebraska
N118 Beadle Center
1901 Vine Street
Lincoln, NE 68588
(402) 472-3626
mwilson13@unl.edu 

=====================================
Postdoctoral Position in Deep Learning and Molecular Image Analysis

The Department of Statistics and the Department of Biochemistry at the
University of Nebraska-Lincoln are pleased to recruit candidates for a
postdoctoral position in molecular image analysis. This position is
supported by the Quantitative Life Sciences Initiative, a university-wide
program supporting the integration of the data and life sciences. We are
seeking candidates with expertise in data generated by molecular imaging
techniques (e.g. X-ray crystallography, cryo-EM, XFEL microcystallography,
electron diffraction), computer science, statistics and machine learning,
who have demonstrated a high level of skill in image preprocessing,
management, and analysis.

The incumbent will be expected to develop a strong research program in
data science and AI for molecular imaging. Responsibilities will include:
(1) developing methods for processing, segmentation, and analysis of
molecular images derived from diffraction or single particle cryo-EM data,
(2) developing software implementations of novel analytical approaches to
molecular images, and (3) using newly emerging analytical and
computational tools to extract the maximum amount of information from data
produced by modern structural biological imaging modalities (e.g., serial
crystallography, single particle cryo-EM, single particle diffraction).

The Initiative and the Departments of Statistics and Biochemistry will
support successful candidates to establish effective disciplinary and
trans-disciplinary collaborations including integration with existing
research groups; connect with stakeholders, agency, and/or industry
partners; obtain and leverage external and internal support (grants,
fellowships, etc.) for research and teaching activities; publish in
high-quality, high-impact peer-reviewed journals and participate in
scientific meetings and other appropriate activities; and translate
research-based information into learner-centered products.

The successful candidate will be expected to teach at least one regular
course per academic year in molecular imaging and image analysis. In
addition, the successful candidate will participate in program and
curriculum development, including graduate seminars and workshops.

Minimum qualifications: PhD in Statistics, Mathematics, Physics,
Biochemistry, or closely related field. Experience with analysis of data
from molecular imaging experiments, as demonstrated by refereed papers,
presentations, or other completed projects, e.g., PhD thesis. Computing
and methodological skills appropriate to the preprocessing and analysis of
data types with which the candidate has experience.

Preferred qualifications: Demonstrated methodological novelty and creative
ability in one or more area of deep learning and AI applicable to
molecular imaging. This includes, but is not limited to, Bayesian
statistics, Gaussian processes, image analysis, and prediction of optimal
experimental settings and molecular orientation. Collaborative research
experience in structural biology using either X-ray crystallography or
cryo-EM approaches. Proficiency with modern object-oriented programming
languages including C++ and Python. Communication skills, written, verbal
and otherwise, at a level sufficient to interact easily with a broad range
of researchers at UNL, with the academic world more generally, and with
the broader Nebraska scientific community.

To view details of the position and make application, go to
http://employment.unl.edu, requisition F_190163.  Click “Apply to this
job” and complete the information form.  Attach a letter of interest,
curriculum vitae, contact information for three professional references,
and a one-page statement of research interests (attach as “Other
Document”).  Review of applications begins October 31, 2019 and continues
until the position is filled or the search is closed.

As an EO/AA employer, qualified applicants are considered for employment
without regard to race, color, ethnicity, national origin, sex, pregnancy,
sexual orientation, gender identity, religion, disability, age, genetic
information, veteran status, marital status, and/or political affiliation.
See http://www.unl.edu/equity/notice-nondiscrimination.
====================================


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