There are a few graduate research positions available in the Biophotonics Lab, which is located in the newly renovated ABB photonics facility as well as in the McMaster Smart Home for Aging-in-Place (SHAPE) in Westdale. The students will be responsible to build and calibrate different experimental systems including

• ultrafast laser micromachining and additive manufacturing,
• fluorescence microscopy,
• endoscopic imaging,
• optofluidic sensing,
• Smart Home sensor networks.

Each of these projects have different requirements which are outlined below.

It is also part of the Accelerated Master Program in Biomedical Engineering, especially (but not limited to) for students in the iBioMed program.

• Enthusiasm in biomedical engineering research
• Undergraduate degree in photonics engineering, physics, EE/ECE, mechatronics, mechanical engineering, life science/biochemistry,

integrated Sciences, and related fields.

• Strong physics and math academic backgrounds

Interested students should e-mail an updated CV and unofficial transcript to biophotonics@mcmaster.ca.

Please refer to the main research page for general project descriptions as well as related publications which provides details of these projects.

In drug discovery and general biotechnology development, an essential step is to quantitatively measure molecular level interactions with advanced optical imaging of live cells including confocal fluorescence lifetime imaging microscopy (FLIM). Current confocal and FLIM instruments have slow frame rate due to the need for raster scan pixel-by-pixel. Recently, we developed a multiplexed confocal fluorescence microscope capable of scanning 1000 foci points simultaneously, which leads to a 1000 times improvement in acquisition frame rate. In this project, we plan to further developing the multiplexing imaging technology towards new instrumentations such as hyperspectral imaging, multi-dimension imaging fusion, and high speed imaging. We will also investigate new biomedical applications of this technology in the area of drug discovery, viral therapy, metabolism, and neurodegenerative disease.

Requirements:
Good academic track record background in optical imaging, mechatronics, electrical/electronics engineering, experimental physics are highly desired. Past research or professional working experiences in any related areas are a plus. The applicant should have solid hands-on experience in at least one programming language (C/C++, Python, Java, etc.) Knowledge of machine learning/artificial intelligence are welcome.

Urine testing is an essential clinical diagnostic tool. The presence of urine sediments, typically analyzed through microscopic urinalysis or cell culture, can be indicative of many diseases, including bacterial, parasitic, and yeast infections, as well as more serious conditions like bladder cancer. Current urine analysis diagnostic methods are usually centralized and limited by high cost, inconvenience, and poor sensitivity. For example, the microbiology lab at Hamilton General processes over 5000 urine and blood samples per week. Many of these samples require complex and time-consuming processes. To address this challenge, we plan to develop an optofluidic microscope to rapidly analyze a large volume of sample and screen for parasites and different types of blood cells. In this summer research project, we will build and optimize a prototype lensless imager to measure urine and blood samples. We will target motion as a biomarker to detect white blood cells and red blood cells as well as trichomoniasis in these samples. We will develop image analysis algorithms for automated detection and phenotyping of these targets.

Requirements:
Good academic track record background in general life science, microbiology, biochemistry are highly desired. Students from interdisciplinary areas such as the iBioMed, the iSci, and the Art & Sciences programs are welcome. Past research or professional working experiences in any related areas are a plus. The applicant should have solid hands-on experience in wet lab bench work. Knowledge of microscopy are welcome.

In colonoscopy, it is important to monitor the progression or recurrence of suspected cancerous lesions (e.g., polyps). Because the colon is contractile and mobile, however, it is very difficult to relocate a lesion (e.g., a polyp) even during the same procedure. In this project, we are developing a novel panoramic imaging method to build a map of colon lining, during colonoscopy, and combine it with machine learning algorithms to locate and track cancerous and pre-cancerous lesion during endoscopy procedures. This research will make colon cancer screening and treatments more effective.

Requirements:
Good academic track record background in optical imaging, mechatronics, electrical/electronics engineering, experimental physics are highly desired. Past research or professional working experiences in any related areas are a plus. The applicant should have solid hands-on experience in at least one programming language (C/C++, Python, Java, etc.) Knowledge of machine learning/artificial intelligence are welcome.