[Cugapost] Postdoc in Nuclear Physics/Machine Learning at Jefferson Lab

[Lorelei Carlson]

Postdoc Position in Nuclear Physics/Machine Learning at Jefferson Lab

The Jefferson Lab Theory Center invites applications for a postdoctoral
research associate position (Job ID: 11948), funded by the Laboratory
Directed Research and Development (LDRD) fund, to begin in October 2019
(or earlier date). The position will be for a period of two years,
subject to funding availability.

The job will involve conducting research in hadronic and nuclear
physics using machine learning techniques, with the goal of performing
a comprehensive analysis of data samples to may event distributions
observed in particle detectors into a femtometer-scale Monte Carlo
event generator (MCEG), without theoretical assumptions about the
underlying partonic degrees of freedom. The project will involve the
development of the MCEG using neural networks, as well as training
neural networks to map existing detector simulators. The resulting
free of detector acceptance corrections and can be viewed as a data
compactification tool of he femtometer scale physics.

The MCEG will be a new innovative tool to study nucleon structure and
hadronization, especially at low and intermediate energies, and can be
used as a general purpose MCEG in experimental physics for the design
of new detectors. The project is anticipated to enhance the scientific
program of Jefferson Lab at 12 GeV, as well as future projects.


Essential Job Functions:

* Apply state-of-the-art machine learning algorithms to develop the
MCEG and neural network training for detector simulators.

* Disseminate the research through articles in refereed publications,
and presentations at conferences, workshops and seminars.

* Make the software accessible and easy to use for the community.

* A PhD in theoretical or experimental nuclear or particle physics
and a solid publication record.

* Experience in machine learning algorithm applications to particle
and nuclear physics.

* Proficiency in python or a similar language that can use open-source
libraries, such as tensor flow or pytorch, for the software development
is desirable.

* Ability to conduct research both independently and in collaboration
with theorists and experimentalist at Jefferson Lab, and to give
presentations at major conferences and workshops on this work.


Position Requirements:

* A PhD in theoretical or experimental nuclear or particle physics
and a solid publication record.

* Experience in machine learning algorithm applications to particle
and nuclear physics.

* Proficiency in python or a similar language that can use open-source
libraries, such as tensor flow or pytorch, for the software development
is desirable.

* Ability to conduct research both independently and in collaboration
with theorists and experimentalist at Jefferson Lab, and to give
presentations at major conferences and workshops on this work.


Further details can be found at:

https://careers.peopleclick.com/careerscp/client_jeffersonlab/external/jobDetails.do?functionName=getJobDetail&jobPostId=1090&localeCode=en-us

All applicants should upload (a) their cv, (b) research interest/plan and
(c) publication list through JLab's online application tracking system
at https://www.jlab.org/job-openings .

Additionally, 3 letters of recommendation should be sent on your behalf
to Mary Fox at mfox at jlab.org by December 2, 2018.

While not required, applicants may also send a copy of application
materials to Mary Fox electronically. Applicants submitting application
materials to Mary Fox without also having applied online will not be
considered.
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