[Cugapost] Post-doctoral position in machine learning and hadronic physics at CEA Saclay

[Lorelei Chopard]

Post-doctoral position in machine learning and hadronic physics at CEA 
Saclay
(DRF/Irfu and DRT/LIST)

CEA Saclay (France) seeks to hire a post-doctoral fellow, who will join 
both the Nuclear Physics
Division at Irfu and the Artificial Intelligence division at LIST for a 
one-year position, renewable
another year. He will be expected to develop crystal-box machine 
learning algorithms in order to
analyze the data collected this fall and in the spring 2018 with the 
CLAS12 spectrometer at
Jefferson Laboratory (USA).

With its 12-GeV electron beam sent on a fixed target, Jefferson 
Laboratory is the world leading
facility to understand the strong interaction within hadrons and nuclei. 
This fall, the upgrade of the
spectrometer in the Hall B of Jefferson Lab will be completed. After a 
commissioning period, data
will be collected on an unpolarized liquid hydrogen target to study the 
proton structure. The nuclear
physics department at Saclay is a leading institute of the flagship 
experiment dedicated to deeply
virtual Compton scattering, the golden process to perform the tomography 
of nucleons. Because of
the unprecedented statistical accuracy of the data set, it will be of 
outmost importance to keep
systematics uncertainties as low as possible during the analysis.

To achieve this goal, the nuclear physics division is starting a program 
aiming at developing crystalbox
machine learning algorithms to analyze the experimental data. Indeed 
machine-learning
techniques, such as neural networks, have proven to be more efficient in 
terms of selection and
classification of physics events. However, the impossibility to recover 
the logic behind the decision
makes them delicate to use in a physics analysis. Unlike neural 
networks, crystal-box algorithms
make it possible justify a result and simplifies the handling of 
uncertainties on the input parameters.
This specificity will allow recovering the logic for validation purposes 
but might also open new
perspectives by improving our understanding of the data. This program 
will be carried out jointly
with the LIST, a world-leading laboratory in the field of machine 
learning at CEA/Saclay.

The primary task of the successful candidate will be to develop an 
algorithm to discriminate DVCS
events from other sources of backgrounds, and extract the corresponding 
cross section in the end.
Applicants should have completed a PhD in experimental nuclear or 
high-energy physics, have
significant expertise in object-oriented programming. Experience with 
machine-learning algorithms
or tensor flow would be beneficial.

Applications should include:
- A 2-page cover letter with a description of previous work experience.
- An academic CV including a list of the candidate’s most relevant 
publications, analysis notes or
talks given in international conferences or workshops.
- 2 recommendation letters.

Applications should be sent to maxime.defurne at cea.fr. More information 
may be requested at the
same e-mail adress.
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