20 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
Most days mix hands-on experiments with data work. You spend mornings running or calibrating instruments, setting up detectors, lasers, vacuum systems or electronic test rigs, then collecting data from those runs.
Afternoons often go to data analysis on Linux or Windows, writing code in C++ or using Git for version control, meeting students, and writing papers or grant proposals. Some days are teaching or giving talks; other days are mostly reading literature and planning the next experiment.
Expect Linux for data acquisition and control, C++ for fast instrument code, and Git for code and paper versioning. Large labs often use cloud services like Microsoft Azure or Amazon Web Services (AWS) to store and process big datasets.
You may also see Microsoft Access for small databases, and specialized instrument control software tied to oscilloscopes, mass specs, or FPGA tools. Learning basic sysadmin on Linux and cloud basics helps a lot.
Start with a physics bachelor’s degree where you get lab courses and learn mechanics, electromagnetism, and quantum basics. Take programming (C++), statistics, and Linux system courses if available.
Join a research group as an undergraduate assistant to learn experiments, instrument calibration, and how to write short reports. For most research jobs you’ll need a PhD; get experience writing small proposals and mentoring undergraduates.
The U.S. Bureau of Labor Statistics (BLS) reports about 20,430 people employed as physicists and astronomers in 2025. The median wage is $172,250 per year, the lowest tenth is $82,110, and the top tenth is $274,110. These are national figures from BLS.
Actual pay varies widely: academia and government labs often pay less than industry or tech companies, and seniority, location, and grant support change your take-home.
AI is usually used for data analysis: filtering noise, pattern recognition, or controlling adaptive experiments. You keep raw data and processing code (Git) so results are reproducible, and you test AI models on known calibration datasets first.
Always validate model outputs against physical expectations and error bars. Document hyperparameters and version the models in the same repository as analysis code. If you use cloud GPU on Azure or AWS, secure the data and follow your lab’s data policy.
Experimental physicists design and run physical experiments, build or maintain instruments, collect measurements, and compare with theory. Theoretical physicists focus on mathematical models and predictions but rarely run lab hardware.
Engineers design products and systems for specific uses and worry about cost, manufacturability, and standards. Experimental physicists often prototype new instruments and publish fundamental findings in journals rather than produce market-ready devices.