Summer Internship

Summer Internship

For those interested in engaging in practical applications of nuclear science and radiation detection, we offer a six week summer internship where you can assist in projects that further Radwatch/DoseNet work and develop your own research skills. If you're interested in joining our lab for the summer at UC Berkeley, check out the "How can you join?" section below and join our Discord server!


What can you expect?

This internship can be an incredible experience for those interested in radiation detection, electronics, and software development. Interns can choose which projects they work on according to their interests, we can assign you to one as well, or you can brainstorm your own ideas! We use Python to facilitate our research and operate our devices, however, there is no need for any prior knowledge of Python nor nuclear physics, we will teach you everything you need to know for the projects you choose to work on. This years internship is 6 weeks long, spanning from June 26th to August 4th where 24 hours of progress is expected per week. We prefer that students come in person on certain days of the week, but scheduling can be flexible and virtual accommodations can be made. The internship is unpaid.


Projects for this summer:


  1. We will be taking our DoseNet devices across the Bay Area to map out radiation levels around us.

Exploring hardware upgrades:

  1. Magnetometer
  2. Accelerometer
  3. Proximity sensors
  4. Radiation sensors
  5. Weather sensors
  6. DoseNet code maintenance

Web-based data visualizations:

  1. Visualizing spectra and graphs using Dash/Plotly
  2. Publishing our work onto the website

Data Analysis:

  1. Spectral analyses
  2. Correlation analyses


How can you join?

There is a preliminary assignment that you may look over and attempt to solve. Relevant tutorial notebooks and the assignment notebook can be found in the Google Drive link provided below. Python 3 and Jupyter Notebook is the coding language and IDE respectively that we will using for our data analyses projects. You need not install the Jupyter Notebook kernel for this assignment, as the notebooks can be run on Google Collab, just make sure to open the notebook in a new window and click the three dots at the top, right-hand corner, select "Open in new window", then click "Copy to Drive" to edit your own copy of the notebook. However, if you wish to install Jupyter Notebook, you may do by following their install instructions. Once you have completed the 5-ASSIGNMENT_AQBarChart.ipynb to the best of your ability, you may submit the .ipynb file with the Google Form below. Once you do, we will email you shortly to arrange a quick interview with you.

Please note that there is no need to complete nor optimize your solution in the assignment notebook. If you are unfamiliar with Python 3, you may simply describe how you would solve each problem to the best of your ability in a Python comment (denoted with a # at the beginning of each line you want to comment), or a markdown cell. We simply want to assess your initiative to learn new things and your openness to asking questions when you have them. If you have any questions, you may join the Discord server that we have set up to assist you in the admissions process.

The Google Form submission deadline is May 21st, 2023.

Jupyter Notebooks

Discord Server

Google Form

Good luck!