Yes, any of the non-probability sampling techniques can be used, e.g. convenience, snowballing, judgmental, etc., especially since it is usually not possible implement probability sampling techniques (i.e. most do not have access to a complete list of the research population and/or sampling frame).

For Thesis, we should always prefer probability sampling as our sample should not be biased and represent the population

While probability sampling is generally preferred for quantitative studies to ensure representativeness and enable generalization of results, there may be situations where non-probability sampling is necessary or more practical. These situations could include limited resources, time constraints, or difficulty accessing the target population. However, it's important to recognize that non-probability sampling introduces potential biases and limitations in generalizing your findings to the broader population. If you choose a non-probability sample, carefully consider these limitations and discuss them in your study's conclusions.

See my response to https://www.researchgate.net/post/Is_there_a_need_to_calculate_sample_size_in_non-probability_sampling_AND_What_is_the_rationale. 

Also, please see my comment at https://www.researchgate.net/publication/367323941_Variance_Estimation_for_Probability_and_Nonprobability_Establishment_Surveys_An_Overview/comments

Controlling bias, as I mentioned in the sample size question noted above, is the problem to address.

Also, you might consider the Official Statistics application in the following:

"Application of Efficient Sampling with Prediction for Skewed Data," JSM 2022: 

https://www.researchgate.net/publication/362370770_Application_of_Efficient_Sampling_with_Prediction_for_Skewed_Data