Morten Hjorth-Jensen

Professor of Physics


  • Joined the laboratory in January 2012
  • Theoretical nuclear physics
  • Contact information

Education and training

  • MS, Science, Norwegian University of Science and Technology 1988
  • PhD, Theoretical Nuclear Physics, University of Oslo 1993


I am a theoretical physicist with an interest in many-body 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 Schröedinger’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 many-body methods. This also includes includes algorithms from Quantum Information Theory and their applicability for 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 additional 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 machine learning 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 high performance 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 many-body 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 21st century.

Scientific 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).Awards