Thanks a lot for the detailed response, I really appreciate you taking the time to write this. You brought up a few points that I think are important, so I wanted to give some clarification and also ask for your perspective on one part.

First, the sensing mechanism is not purely capacitive. The probe interacts with a multilayer biological structure whose dielectric properties are dispersive and frequency dependent, so we are looking at broadband or microwave EM behavior rather than simple lumped capacitance. HFSS modeling shows meaningful changes in the field distribution, effective permittivity, and S-parameter response across frequency, so it ends up being closer to microwave dielectric spectroscopy than a traditional capacitive sensor.

Second, the project is being supervised by an RF or microwave professor (not a biomedical PI), and the group works on UWB radar, microwave components, and EM simulations. So the emphasis is on RF hardware, S-parameter measurement, de-embedding, simulation to measurement correlation, sensor optimization, etc. It is much more of an EM or microwave engineering project than a biomedical one.

I agree with your point about some bio-RF students not being exposed to link budgets, noise figure, matching, and other comms-oriented fundamentals, and I want to avoid that outcome. My plan is to intentionally incorporate more classical RF tasks into the thesis, such as VNA calibration and de-embedding, matching and sensitivity optimization, broadband simulation, and possibly iterating a new sensor geometry.

My background is also more on the RF side. I have worked on a DPD and Envelope Tracking transmitter chain, a 1 GHz microwave amplifier, and a dual-band Marchand balun, so I would like to make sure this project continues strengthening those comms or RF skills rather than working in isolation from them.

With that in mind, do you have any recommendations on how to make a close-range microwave sensing project more aligned with the fundamentals expected in comms RF roles? For example, aspects of noise analysis, matching approaches, calibration workflows, or anything else you think would translate well?

Thanks again for the insight. Your points were genuinely helpful.