Charles Nicklas Christensen

Charles Nicklas Christensen

PhD Candidate
Computer Vision & AI

University of Cambridge


Charles Nicklas Christensen is a final year PhD student at University of Cambridge. His research is in applying computer vision methods to optical microscopy using deep learning, for instance ML-SIM and ERnet . ML-SIM is a method to train neural networks to reconstruct structured illumination microscopy images based on physically modelled synthetic training data.

Other interests include mathematical modelling, artificial intelligence and deep learning.

Download Charles Christensen’s resumé.

  • Computer Vision
  • Image Analysis
  • Deep Learning
  • Super-resolution
  • Computational Imaging
  • PhD in Computational Imaging, Est. 2022

    University of Cambridge

  • MRes in Image Processing and Sensing, 2018

    University of Cambridge

  • MScEng in Mathematical Modelling and Computation, 2017

    Technical University of Denmark

  • BScEng in Physics and Nanotechnology, 2015

    Technical University of Denmark


Data Scientist
Jul 2021 – Oct 2021 London, UK
Internship project in which I collaborated with Tesco’s computer vision team on large-scale image classification for product recognition at the till to prevent theft.
  • Metric learning
  • Transformers
  • Pytorch
  • Pandas
  • TensorRT
Co-founder & Head of Data
Jun 2018 – Present Cambridge, UK

Part-time involvement in non-profit organisation. Started as a research project doing my MRes and spun out in 2019 with five other PhD students.

  • Open-source hardware design of air quality monitor.
  • Deployment of sensor networks in several countries across South America, Africa, Europe and Asia.
  • More information
  • Big data
  • Data visualisation
  • Python
  • Scikit-learn
  • MongoDB
Research Associate
Physical Computation Lab
Jul 2018 – Jan 2019 Department of Engineering, Cambridge University, UK
Internship project on large-scale image recognition using metric learning
Research Assistant
Danish Fundamental Metrology
May 2017 – Aug 2017 Lyngby, Denmark
Researched and developed a machine learning system for a novel laser based fire detector. This involved optical engineering and signal processing using machine learning techniques.
  • Signal processing
  • Time-series classification
  • Support vector machines
Data Consultant
Sep 2017 – Dec 2016 Lyngby, Denmark

Alongside my MSc studies, I worked part-time on a number of consultant jobs including:

  • Developed a system for image analysis of a continuous video feed at a production facility overseen by food industry technology provider GEA.
  • Performed data mining on a big dataset for Danish television broadcaster TV2 with results used in a program about fitness level.
  • Video tracking
  • Data mining
Research Assistant
Department of Photonics Engineering
Sep 2017 – Jul 2017 Technical University of Denmark
Further researched the topic of my BSc thesis and wrote it up as a publication.
  • Markov processes
  • Quantum mechanics
  • Algebraic computation
  • Mathematica


(2021). ML-SIM : Universal reconstruction of structured illumination microscopy images using transfer learning. In Biomedical Optics Express.

PDF Cite DOI GitHub

(2021). Simple and robust speckle detection method for fire and heat detection in harsh environments. In Applied Optics.


(2018). Driving-induced population trapping and linewidth narrowing via the quantum Zeno effect. In Physical Review A.


Academic Projects