202 FRIB Graduate Brochure

Morten Hjorth-Jensen
Professor of Physics
Keywords: Nuclear Many-Body Theory, Computational Physics Machine
Learning and Quantum Computing
Theoretical Nuclear Physics
About
• MS, Science, Norwegian University of Science and
Technology 1988
• PhD, Theoretical Nuclear Physics, University of Oslo
1993
• Joined the laboratory in January 2012
• hjensen@frib.msu.edu
Research
I am a theoretical physicist with an interest in manybody
theory in general, and the nuclear many-body
problem and nuclear structure problems in particular.
This means that I study various methods for solving
either Schroedinger’s equation or Dirac’s equation for
many interacting particles, spanning from algorithmic
aspects to the mathematical properties of such methods.
The latter also leads to a strong interest in computational
physics as well as computational aspects of quantum
mechanical methods. A large fraction of my work, in close
collaboration with colleagues at the FRIB and worldwide,
is devoted to a better understanding of various quantum
mechanical algorithms. This activity leads to strong
overlaps with other scientific fields. Although the main
focus has been and is on many-body methods for nuclear
structure problems, I have also done, and continue to do,
research on solid state physics systems in addition to
studies of the mathematical properties of various manybody
methods. The latter includes also algorithms from
Quantum Information Theories and their applicability to
for example nuclear physics problems. Studies of Machine
Learning algorithms applied to the nuclear many-body
problem as well as tools to analyze experimental data
from FRIB, are also topics I work on.
To understand why matter is stable, and thereby shed
light on the limits of nuclear stability, is one of the
overarching aims and intellectual challenges of basic
research in nuclear physics and science. To relate the
stability of matter to the underlying fundamental forces
and particles of nature as manifested in nuclear matter is
central to present and planned rare isotope facilities.
Examples of important properties of nuclear systems that
can reveal information about these topics are masses (and
thereby binding energies) and density distributions of nuclei.
These are quantities that convey important information
on the shell structure of nuclei with their pertinent magic
numbers and shell closures, or the eventual disappearance
of the latter away from the valley of stability. My research
projects, in strong collaboration with other theorists and
experimentalists at FRIB, aim at understanding some of
the above topics. These projects span from computational
quantum mechanics with various many-body methods, via
studies of quantum information theories and their relevance
to studies of nuclear many-body systems to machinelearning
applied to the same problems as well as ways to
interpret data from nuclear physics experiments.
Topics discussed here for possible thesis projects aim at
giving you knowledge and insights about the physics of
nuclear systems as well as an in depth understanding of
many-body methods. This includes also developing your
competences and skills on highly relevant computational
methods, from central numerical algorithms to highperformance
computing methods. The following list
reflects some of our research possibilities:
• Computational quantum mechanics, computational
physics, and many-body methods applied to
studies of nuclear systems, with strong overlaps
with experiment
• Development of time-dependent many-body
theories of relevance for fusion and fission studies
• Quantum information theories and nuclear manybody
problems
• Studies of dense nuclear matter and neutron stars
• Machine-learning algorithms applied to nuclear
many-body methods
• Machine-learning algorithms applied to the
analysis of nuclear physics experiments
Theoretical nuclear physics is a highly interdisciplinary
field, with well-developed links to numerical mathematics,
computational physics, high-performance computing,
and computational science and data science, including
modern topics like quantum information theories,
statistical data analysis, and machine-learning. The skills
and competences you acquire through your studies
give you an education that prepares you for solving and
studying the scientific problems of the 21 st century.
Selected Publications
Morten Hjorth-Jensen, M.P. Lombardo and U. van Kolck,
Lecture Notes in Physics, Editors M. Hjorth-Jensen, M.P.
Lombardo and U. van Kolck, Volume 936, (2017).
Morten Hjorth-Jensen, Computational Physics, an
Introduction, IoP, Bristol, UK, 2019
Fei Yuan, Sam Novario, Nathan Parzuchowski, Sarah
Reimann, Scott K. Bogner and Morten Hjorth-Jensen., First
principle calculations of quantum dot systems, Journal of
Chemical Physics, 147,164109 (2017).
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