Ph.D. and post-doc positions are available to work on fluorescence microscopy related projects in joint research group with University of Texas at Dallas and UT Southwestern Medical Center at Dallas. The projects aim to develop novel imaging modalities, image processing and data analysis methods for fluorescence microscopy live cell experiments in particular single molecule detection.

Signal processing engineers usually use high level languages to develop advanced algorithms for new radars and to determine the optimal parameters for these algorithms. The long execution times due to computational complexity and/or very large data sets hinders an efficient engineering development workflow. Rapid prototyping tools such as parallel MATLAB can enable a software design workflow that helps the development of radar prototypes by providing interactivity and reducing the execution time of the algorithms under test.

The ability to learn about a stochastic process from noisy observations is fundamental to many applications. In order to track a dynamic process, typical knowledge representation is the state space model such as a linear Gauss Markov model, where efficient algorithms exist to perform state estimation under many different model assumptions. However, for meta level tracking, we are not only interested in the state estimation, but also temporal and structural classification of the process.

Polarimetric Synthetic Aperture Radar (POLSAR) data is an important source for many operational remote sensing applications. Segmentation and classification of image data are important tasks for POLSAR data analysis and interpretation, which often requires human interaction. In this thesis, spectral graph partitioning methodology is used to exploit both the polarimetric attributes of pixels, and the visual aspect of the image data through visual cues.

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