CV

Please find an outline (and PDF) of my CV, focusing on my academic background. For a more general or industry-focused CV, feel free to contact me for the latest version.

Basics

Name Jacob Tutt
Label PhD Candidate | 21cm Cosmology | Bayesian Machine Learning
Email jlt67@cam.ac.uk
Url https://jacobtutt.github.io
Summary Astrophysics PhD candidate at the University of Cambridge, specialising in Bayesian inference, machine learning, and high-performance computing. My research focuses on developing scalable statistical and computational methods to extract cosmological insights from large-scale datasets, with a particular emphasis on the 21cm line observations in the emerging Era of Exascale Radio Astronomy.

Work

  • 2025.10 - Present
    Undergraduate Supervisor
    University of Cambridge
    • Natural Sciences, Physics 1A - supervising small teaching groups of undergraduates
    • MPhil Data Intensive Science, Statistical Methods - supervising graduate students in key statistical theory and methods behind data science and machine learning.
  • 2025.10 - Present
    PhD Researcher
    Cavendish Astrophysics, University of Cambridge
    • Research focused on developing advanced Bayesian machine learning pipelines for radio cosmology
    • Particularly the detection of the 21cm signal redshifted from the Cosmic Dawn and Epoch of Reionisation
    • Supervised by Dr Eloy de Lera Acedo
  • 2024.10 - 2025.09
    Graduate Researcher
    Institute of Astronomy, University of Cambridge
    • Research focused on optimisation of probabilistic unsupervised machine learning methods for near-field cosmology
    • Indentifying stellar subpopulations from within chemo-dynamical datasets constructed from GAIA, APOGEE and GALAH
    • Enabling robust, uncertainty-aware insights into the formation and assembly history of the Milky Way
    • Building pipelines designed for the volume and dimensionality offered by the next generation of surveys such as WEAVE and 4MOST

Education

  • 2025.10 - Present

    Cambridge, UK

    PhD Astrophysics
    University of Cambridge, UK
  • 2024.10 - 2025.09

    Cambridge, UK

    MPhil Data Intensive Science
    University of Cambridge, UK
    Graduate course jointly taught across DAMTP, the Institute of Astronomy, and the Cavendish Laboratory, covering statistical inference, deep learning, and advanced software development for large-scale astronomical data. Focusing on applications to theoretical astrophysics including galactic archaeology, cosmology, and gravitational waves.
    • Grade: High Distinction (85%)
  • 2021.10 - 2024.07

    Durham, UK

    BSc Physics
    Durham University
    Took modules in pure mathematics, theoretical physics, and astrophysics, complemented by research projects in computational modelling, statistical inference, and observational techniques conducted within the Centre for Extragalactic Astronomy and the Institute for Computational Cosmology.
    • Grade: First Class (4th in Cohort)
  • 2019.09 - 2021.08

    Hampshire, UK

    A-Level
    Churcher’s College
    Mathematics, Further Mathematics, Physics, Chemistry
    • Grades: 4*s

Awards

  • 2025.10.01
    Cavendish Scholarship
    Cavendish Laboratory, University of Cambridge
    Selected for the prestigious Cavendish Scholarship Programme.
  • 2025.10.01
    Harding Distinguished Postgraduate Scholar
    University of Cambridge
    Selected for the Harding Distinguished Postgraduate Scholars Programme.
  • 2023.07.01
    Durham Physics Award
    Department of Physics, Durham University
    Awarded for Outstanding First-Class Achievement in BSc Physics.

Skills

Programming
Python
PyTorch
TensorFlow
Jax
Bash
Git
Machine Learning
Deep Learning
Unsupervised Learning
Natural Language Processing
Generative Models (VAE, Diffusion, etc)
Fine-Tuning