# Morten Hjorth-Jensen

## Professor of Physics

**About**

- 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

**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 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