Dotphoton is a young and dynamic Zug-based startup with a big vision
We value honesty, team spirit and independence. We love science, photography and escape games. We speak English, French, Russian, Italian, German and Danish.

Together we are aiming for reimagining modern imagery in life-sciences and critical applications

Part-time Administrative Assistant and Bookkeeper

We are looking for someone who is upbeat and excited to work. For this position you will have a range of tasks. The objective of this job is to participate in the daily administrative tasks and bookkeeping of the company. The job responsibilities for this position are as follows:

  • Invoicing Customers
  • Collections of receivables
  • Comfortable with Microsoft Office Programs
  • Bookkeeping
  • Logistics
  • Help with Accounts Payables and financial reports
  • Great customer service with good communication skills
  • Ability to work independently or with a team enviornment
  • Administrative and office duties.
  • Flexibility in day to day tasks while maintaining organization
  • Knowledge in sales, use and income tax. but not required
  • Fluent German and English
Scientific programmer, C++/Python
For an upcoming project, we are looking to hire a scientist or engineer to join our Research and Development team. who will be responsible for pushing the performance of our image processing algorithms and lay the foundation for the use of specialised hardware, such as FPGAs, GPUs, and the Intel Movidius chip. The project requires special attention on reliability and rigorous documentation for both internal and external use.

Part-time work is acceptable (but minimum 50%), and remote work can be discussed.


  • Solid foundation in physics/optics and math. Good working knowledge of statistics, probability theory and information theory is a plus.
  • Excellent scientific programming skills in Python, in particular the NumPy/SciPy/Jupyter tool set.
  • Experience in production-level software development in C++, good routine with unit tests and test-driven development (e.g. Google Test), code documentation (e.g. Doxygen), and profiling for resource and performance assessment.
  • Detailed knowledge about Machine Learning and computational imaging is a strong plus, and experience with TensorFlow or similar will be highly appreciated.
PhD in Computing Science: Data compression at the quantum limit for big-data machine learning applications

The University of Glasgow (UofG) is home to world-leading research in the fields of imaging and computing science and home to the UK Hub for Quantum Imaging Technologies, QuantIC (https://quantic.ac.uk/) who are co-funding this PhD research project in collaboration with a high-tech startup company, Dotphoton (http://www.dotphoton.com/).

You will be part of a unique, world-leading and international research team, with training in cutting edge research techniques involving computational techniques, AI and data compression applied to next generation biological and quantum images.

The Project

We are searching for a highly motivated student to work at the interface between computing science (machine learning and artificial neural networks) and imaging (classical and quantum imaging) in collaboration with a high-tech start-up company.

The aim of this project is to bring together expertise developed within Dotphoton, based in Switzerland, with machine learning and artificial neural networks developed at UofG for imaging applications under study within QuantIC, the UK Hub for quantum imaging based at UofG. One of the main obstacles in applying machine learning to image data is the huge amount of memory required, in particular RAM during the learning process. This is currently proving to be a bottle-neck for the development and deployment of artificial neural networks applied to high pixel density images. As an example, the two UofG supervisors have recently developed an ANN that can decode the random speckle patterns at the output from a multimode fibre and estimate the input, unscrambled image. The technique is very promising yet is currently limited to images sizes of 90x90 pixels.

The main goal is to use/develop Dotphoton's approach to data compression and thus fit up to four times more image data in a GPU memory. We will search for options in which we can either decode on-the-fly, or format the data such that it can efficiently be directly used by the machine learning algorithm. This will be a game-changer for machine learning applied to imaging in all sectors, e.g. lidar, MRI, bio-imaging but also more in general, in any applications where large amounts of data are involved.

Applications will be considered on a rolling basis. For enquires specific to the project, please email Roderick.Murray-Smith@glasgow.ac.uk or daniele.faccio@glasgow.ac.uk.
Funding Notes

Funding is available to cover tuition fees UK applicants for 3.5 years, as well as paying a stipend at the Research Council rate (estimated £14,999 for Session 2019-20).
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