Vacancy:
PhD position
Machine
learning for accurate and efficient uncertainty quantification
in radiological waste characterization
Job
description
Quantifying
the activity levels of activated and contaminated materials, in
various radioactive waste package types and geometries is of
paramount importance for the safe and effective decommissioning of
nuclear installations. This PhD focusses on using probabilistic
modeling, Bayesian data inversion and machine learning (ML) to
develop automated codes for deriving the activity distribution of
radionuclides in radioactive objects from radiological
measurements, accounting for relevant sources of uncertainty. The
latter include the spatial distribution of activities within a
waste package, as this can have large effects on the overall
measurement efficiency and the corresponding total activity
estimates. The work will be primarily focused on inferring the
gamma-emitting radionuclide inventory (in terms of total activity
concentrations of a given object) using gamma spectrometry.
Furthermore, this study will also attempt to develop a method for
the Bayesian inference of the 3D spatial distribution of
radionuclides within the considered objects using an enhanced
gamma dataset (i.e., by using total gamma measurements and angular
segmented gamma scanning) together with X-ray tomography (which
can provide an estimate of the 3D density distribution based on
the obtained linear attenuation coefficients). Lastly, the
potential of completely bypassing the data inversion step by using
ML to directly predict the total object’s activity from the
measured raw gamma spectra will be explored as well.
SCK-CEN, Mol, Belgium
SCK-CEN
is the Belgian nuclear research centre which performs research and
development in the domains of safety of nuclear installations,
radiation protection, Medical and industrial applications of
radiation, etc. The activities of the PhD position are embedded in
this stimulating environment and include data-efficient machine
learning (or surrogate modeling), active learning, Bayesian
optimization, etc.
IDLab, Ghent University - imec, Belgium
IDLab is
a core research group of imec, a world-leading research and
innovation hub in nanoelectronics and digital technologies, with
research activities at Ghent University. IDLab performs
fundamental and applied research on data science and internet
technology, and is, with over 300 researchers, one of the larger
research groups at imec. Our major research areas are machine
learning and data mining; semantic intelligence; multimedia
processing; distributed intelligence for IoT; cloud and big data
infrastructures; wireless and fixed networking; electromagnetics,
RF and high-speed circuits and systems.
Your
profile
We are looking for highly creative and motivated
PhD students with the following qualifications and skills.
· You have
(or will obtain in the next months) a master degree in
Computer Science, Mathematics, Informatics, Engineering or
equivalent, with excellent ('honors'-level) grades.
· You have
strong computer science skills (python, C++, etc.)
· You have a strong interest in machine learning,
and are eager to advance the state-of-the-art.
· Experience with machine learning algorithmic
approaches or frameworks (such as PyTorch, Tensorflow, GPFlow,
etc.) is considered a plus.
· You have excellent analytical skills to interpret
the obtained research results.
· You are a team player and have strong
communication skills.
· Your English is fluent, both speaking and
writing.
