Welcome!

I'm a postdoctoral researcher from Denmark currently working at the University of Zurich (UZH) in Switzerland. I work in cosmology and my research focuses primarily on the use of machine learning techniques to improve and accelerate the cosmological analyses.

I actively maintain and improve the publicly available code CONNECT, of which I am the author. This framework uses neural networks to emulate cosmological observables in order to speed up parameter inferences.

Research Interests

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    Machine Learning

    I use machine learning to accelerate cosmological analyses, enabling full use of data from current and future surveys. My focus is on neural networks to emulate cosmological observables, making models faster and more efficient.

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    Large Scale Structure

    The Universe’s large-scale structure reveals key cosmological information. Accurate, efficient models are essential for interpreting survey data. I focus on simulations and emulators to model structure and improve understanding of the underlying cosmological model.

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    Decaying Dark Matter

    Decaying dark matter may ease tensions in cosmological data. I aim to explore its full parameter space and implications by implementing a general model in an Einstein-Boltzmann solver, ensuring robust and efficient analyses.

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    Statistics

    Statistical tools maximize information from cosmological data. I focus on Bayesian and frequentist approaches, especially profile likelihoods, which I have implemented in cosmological analyses to improve robustness and extract reliable constraints.

News

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    Seminar at SDU

    I will be giving a seminar at the University of Southern Denmark (SDU) on the 6th of October 2025 on the use of machine learning in cosmological parameter inference, and I will subsequently host a workshop of basic usage of TensorFlow and my CONNECT framework.

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    Postdoctoral Position

    I officially started my postdoctoral position at the University of Zurich (UZH) in Switzerland. The position is funded by a two-year fellowship from the Carlsberg Foundation. I am very excited to start this new chapter of my career and work with the very talented people at UZH.

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    PhD Defense

    I successfully defended my PhD thesis and graduated from the Graduate School of Natural Sciences at Aarhus University. The thesis is titled "Making everything connect - optimising cosmological inference through emulation" and can be found here.

  • Carlsberg Foundation

    Carlsberg Foundation

    I was awarded a two-year postdoctoral fellowship from the Carlsberg Foundation to be carried out at the University of Zurich. The fellowship starts in March 2025, and I am very excited to start this new chapter of my career. I will be continuing my work on using machine learning techniques to improve cosmological analyses with focus on large-scale structure.

Places visited

About

Background

I am originally from a town called Fredericia in Denmark and I completed my Bachelor's and Master's degrees in Physics at Aarhus University. I have always been fascinated by the Universe and its mysteries, and I have known from a very early age that I wanted to pursue a career in astronomy and cosmology. This led me to pursue a PhD in Cosmology at Aarhus University, where I focused on using machine learning techniques to improve and accelerate cosmological parameter inference.

It was during my PhD studies that I developed my CONNECT framework, which uses neural networks to emulate cosmological observables in order to speed up parameter inferences. This framework has been publicly available since 2022 and has been used by several research groups around the world. I am very proud of this work and I am excited to continue developing and improving the framework in my future research.

Research Interests

My research interests lie primarily in the intersection of cosmology and machine learning. I am particularly interested in using emulators to accelerate cosmological analyses, since the computation of theoretical models can be very time-consuming. By using emulators, we can significantly speed up the analysis process and make it possible to explore larger parameter spaces and more complex models.

I am also interested in the use of machine learning techniques to fully utilise large-scale structure data from current and future surveys. Large-scale structure is a powerful probe of cosmology, but it is also very complex and difficult to model accurately. I believe that machine learning can enable us to extract more information from large-scale structure data and improve our understanding of the underlying cosmological model.

In addition to my work on emulators and large-scale structure, I am also interested in the use of statistical methods in cosmology. I have a particular interest in the use of profile likelihoods, which are a frequentist approach to parameter inference. I have used profile likelihoods in several of my research projects and I believe that they are a very good addition to the more commonly used Bayesian methods.

Exploring alternative cosmological models is another area of interest for me. I am particularly interested in models that can help to alleviate the tensions that exist between different cosmological datasets. One such model that I am currently working on is decaying dark matter, which has shown promise in addressing some of these tensions.

Hobbies

Outside of academia, I enjoy a variety of hobbies. I play a lot of music, both on the drums and the piano, and I have even completed a music education programme in classical percussion before my university studies. I have played in numerous orchestras and bands over the years, and I now focus more on my progressive metal band, Advocacy, where I play the drums.
Besides my more serious musical endeavours, I also find it entertaining to learn to play odd instruments from time to time, so my collection of instruments is quite diverse.

I love to travel and have been fortunate enough to visit many amazing places around the world across five continents. A hobby of mine is to collect miniature figurines of famous buildings and landmarks from the places I visit, so my collection is always growing.

Skills

Technical Skills

  • Python: Proficient in Python for data analysis, machine learning, and scientific computing.
  • C++: Experienced in C++ for command-line tools and performance-critical applications.
  • TensorFlow: Skilled in using TensorFlow for building and training neural networks, utilising both CPU and GPU resources.
  • Cosmological Simulations: Familiar with cosmological simulation codes and analysis tools.
  • Statistical Methods: Knowledgeable in Bayesian and frequentist statistical methods for data analysis.

Soft Skills

  • Communication: Effective communicator, both in writing and verbally, with experience presenting research at conferences and seminars.
  • Collaboration: Team player with experience working in collaborative research environments.
  • Problem-Solving: Strong analytical and problem-solving skills, with the ability to tackle complex research questions.
  • Time Management: Able to manage multiple projects and deadlines effectively.
  • Adaptability: Flexible and adaptable to new challenges and environments.

Resume

Education and research

  1. University of Zurich - Postdoctoral researcher in Cosmology

    2025 — Now

    Postdoctoral researcher in Cosmology at the University of Zurich (UZH) in Switzerland. The position is funded by a two-year fellowship from the Carlsberg Foundation. The research focuses on using machine learning techniques to improve and accelerate cosmological parameter inference, with a focus on large-scale structure and simulations.

  2. Aarhus University - PhD degree in Cosmology

    2020 — 2025

    PhD degree in Cosmology with focus on using machine learning techniques to improve and accelerate cosmological parameter inference. The PhD included a 2-month research stay at the University of Sussex in the UK. The PhD thesis is titled "Making everything connect - optimising cosmological inference through emulation" and it can be found here.

  3. Aarhus University - Master's degree in Physics

    2019 — 2022

    Master’s degree in Physics with a broad range of courses (including cosmology, quantum mechanics, particle physics, and statistical mechanics). Completed in three years, with the final two integrated into PhD studies. A separate master’s thesis was not required; instead, I submitted a PhD progress report.

  4. Aarhus University - Bachelor's degree in Physics

    2016 — 2019

    Bachelor's degree in Physics with Astrophysics as an elective. Alongside the bachelor's degree, I completed an extracurricular research programme of 30 ECTS focusing on various research fields within physics.

Teaching

  1. Student supervision at Department of Physics and Astronomy, Aarhus University

    2023 - 2024

    Partial supervision of Bachelor's and Master's students in our cosmology research group.

  2. TA at Department of Physics and Astronomy, Aarhus University

    2023, spring

    Teaching assistant for the course "Galaxies and Cosmology".

  3. TA at Department of Physics and Astronomy, Aarhus University

    2022, spring

    Teaching assistant for the course "Galaxies and Cosmology".

  4. TA at Department of Physics and Astronomy, Aarhus University

    2021, fall

    Teaching assistant for the course "Mechanics and Thermodynamics".

  5. TA at Department of Physics and Astronomy, Aarhus University

    2021, spring

    Teaching assistant for the course "Electromagnetism and Optics".

  6. TA at Department of Physics and Astronomy, Aarhus University

    2020, fall

    Teaching assistant for the course "Relativity and Astrophysics".

  7. TA at Department of Geoscience, Aarhus University

    2019, fall

    Teaching assistant for the course "Mechanics and Thermodynamics".

  8. TA at Department of Geoscience, Aarhus University

    2018, fall

    Teaching assistant for the course "Mechanics and Thermodynamics".

Talks and seminars

  1. SDU, Odense (Planned)

    2025, Oct

    Seminar on the use of machine learning in cosmological parameter inference with a subsequent workshop on basic usage of TensorFlow and my CONNECT framework.

  2. COSMO'24, Kyoto

    2024, Oct

    Presentation of new and improved applications of the CONNECT framework at the COSMO'24 conference.

  3. UZH, Zürich

    2023, Oct

    Seminar on the CONNECT framework, its structure, and its applications in cosmology.

  4. COSMO'23, Madrid

    2023, Sep

    Presentation of my CONNECT framework and its applications in cosmology at the COSMO'23 conference.

  5. UNSW, Sydney

    2022, Dec

    Presentation of the use of emulation to do profile likelihoods at The Dark Side of the Universe conference.

  6. School of Mathematical and Physical Sciences, University of Sussex, Brighton

    2022, Oct

    Seminar on the CONNECT framework and its applications in cosmology.

  7. COSMO'22, Rio de Janeiro

    2022, Aug

    Poster presentation of my CONNECT framework at the COSMO'22 conference.

  8. TTK, RWTH Aachen

    2022, Feb

    Seminar on the use of machine learning in cosmological parameter inference.

  9. NBIA, University of Copenhagen

    2021, July

    Presentation during Summer School on Neutrinos: Here, There, Everywhere.

Coding skills

  • Python
    90%
  • C++
    60%
  • Bash
    70%
  • Swift
    60%
  • HTML, CSS, Javascript
    50%

Citation Metrics

  • Inspire HEP Summary

    Citable

    Papers N/A Citations N/A Citations per paper N/A h-index N/A Self-citation rate N/A

    Published

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Publications

Resources

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Contact

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