Charles Nicklas Christensen

Charles Nicklas Christensen

Computer Vision & AI

University of Cambridge


Charles Nicklas Christensen is a researcher specialising in computer vision, with experience in deep learning, super-resolution, image segmentation, and image classification. While working towards his PhD at the University of Cambridge, he explored computer vision techniques in the context of optical microscopy using deep learning. Some of the methods he has contributed with include ML-SIM , ERnet and VSR-SIM .

ML-SIM is a method that utilises neural networks to reconstruct structured illumination microscopy images, relying on physically modeled synthetic training data [Biomed. Opt. Express (2021) , Nature Comms. (2022) ]. ERnet is an image segmentation model for endoplasmic reticulum images that employs an architecture inspired by the vision transformer [Science Advances (2020) , Nature Methods (2023) ].

Within the industrial domain, he has contributed to the development and implementation of scalable deep learning solutions. During his time at Tesco Technology, he focused on metric learning to enable large-scale product recognition in retail environments. While at ONI, he has developed and deployed deep learning methods for spectral demixing, image segmentation, and super-resolution reconstruction in the setting of big data.

His other interests include physics, mathematical modelling, artificial intelligence and deep learning across various applications.

Download Charles Christensen’s resumé.

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


AI/ML Scientist
Oct 2023 – Present London, UK
Machine learning research scientist in the AI/ML team at GSK.
  • Deep learning
  • Computer vision
  • Pytorch
Machine Learning Research Scientist
Oxford Nanoimaging, ONI
Apr 2022 – Oct 2023 Oxford, UK
Developed and deployed deep learning methods for spectral demixing, image segmentation, and super-resolution towards new product offerings. This involved R&D and MLOps. In addition to this, I have also contributed to a web-based acquisition and analysis software which involves several technologies spanning front-end, back-end and deployment.
  • Python
  • C++
  • Image processing
  • Deep learning
  • Pytorch
  • CNNs
Co-founder & CTO
Wizion AI
Oct 2019 – Sep 2022 Cambridge, UK
Together with a colleague from my research group, I co-founded Wizion AI, a company offering image processing products utilising deep learning methods inspired by research in our lab. In 2020, we were selected for the Cambridge Accelerate accelerator programme at the Judge Business School. After three years of efforts towards the development of two products, we decided to shift our focus to other opportunities.
  • Computer vision
  • MLOps
  • Image processing
Computer Vision Research Scientist
Tesco Technology
Jul 2021 – Oct 2021 London, UK
Within the Tesco Data Science department, I worked with the 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
I worked with Professor Phillip Stanley-Marbell, head of the Physical Computation Lab. In the research project, I modelled lightness illusions in the human visual system by attempting to reproduce them with artificial neural networks.
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


(2023). ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology. In Nature Methods.

PDF Cite DOI GitHub

(2022). MAI-SIM: interferometric multicolor structured illumination microscopy for everybody. In Nature Communications.

PDF Cite DOI GitHub

(2022). Spatio-temporal Vision Transformer for Super-resolution Microscopy. On arXiv.

PDF Cite DOI GitHub

(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.


Academic Projects