Industrial NSERC Alliance Postdoctoral Fellowship in AI in Health
The IMPACT lab at Concordia University has a PDF position titled “Development of novel machine learning algorithms for registration of point clouds and tracking surgical tools.”
Minimum Required Qualifications
- PhD in ECE, CS or BME
- Excellent background in one of these areas:
- Image-guided interventions
- Computer vision
- Machine learning
- Deep learning
- Image Processing
- Proven academic track record of publications in one of the fields above
About IMPACT (Image Processing And Characterization of Tissue) lab
Research at IMPACT is focused on developing novel medical image analysis techniques to improve healthcare. IMPACT lab is directed by Dr. H. Rivaz, an Associate Professor and Research Chair in Medical Imaging with Deep Learning. He was selected as a Rising Star in biomedical imaging in 2022 by QBIN. He has been an Associate Editor of IEEE TMI since 2017, IEEE TUFFC since 2018, and IEEE OJBME since 2021. He was a member of the Organizing Committee of IEEE EMBC 2020 (Montreal, Canada), IEEE ISBI 2021 (Nice, France), and IEEE IUS 2023 (Montreal, Canada). He has served as an Area Chair (AC) of MICCAI for six years from 2017 to 2023 and was an AC of IPCAI 2022. He co-organized two tutorials on advances in ultrasound imaging at IEEE ISBI 2019 and 2018. He also co-organized the Seg-CURIOUS 2022, CURIOUS 2019, and CURIOUS 2018 Challenges on registration of ultrasound and MRI in neurosurgery, and the CereVis 2018 Workshop on Cerebral Data Visualization, all in conjunction with MICCAI. IMPACT lab has an excellent track record of training outstanding graduate students and postdoctoral fellows. A state-of-the-art NVIDIA A100 server is dedicated solely to the members of the IMPACT lab, as well as several imaging equipment.About Think Surgical
THINK Surgical, Inc., is a California-based technology innovator that develops and markets orthopedic robots. THINK Surgical robots are open platforms providing support for implant brands from multiple manufacturers, enabling the choice of the implant to be driven by the surgeon. The PDF will work in close collaboration with Tmini Systems, and THINK Surgical (1275 Avenue des Canadiens, Montreal, QC H3B 0G4). The PDF is presented with the opportunity to work either at the THINK Montreal office or the IMPACT lab.Fellowship Duration
The fellowship is for 2 years. A one-year contract will be signed first, which can be extended for an additional year. It is possible to extend the fellowship beyond that period.Diversity Statement
Concordia University is committed to Employment Equity and encourages applications from women, Aboriginal Peoples, visible minorities, ethnic minorities, and persons with disabilities.How to Apply
Please send an email to Dr. Rivaz (hrivaz@ece.concordia.ca) with the title “PDF position with THINK Surgical”.PhD Student Position in Machine Learning and Deep Learning in Ultrasound Imaging
Dr. Hassan Rivaz is currently accepting applications for a PhD student at the intersection of ultrasound imaging and machine learning starting Sept. 2023. The project is in collaboration with Dr. Jonathan Afilalo.
Minimum required qualifications
- Masters in ECE, CS or BME
- Background in one of these fields: Deep learning, computer vision, machine learning, ultrasound imaging
- Good writing and communication skills
Submission process:
Send curriculum vitae to Drs. Rivaz (hrivaz[at sign]gmail.com) and Afilalo (jonathan.afilalo[at sign]mcgill.ca)About IMPACT (Image Processing And Characterization of Tissue) lab
The fellow will be based at the IMPACT (Image Processing And Characterization of Tissue) lab at Concordia University. The lab is equipped with an NVIDIA A100 server, Verasonics 256 and Alpinion E-CUBE R12 ultrasound machines with access to RF and channel data, a Supersonics Imagine ultrasound machine with access to RF data, and NDI Polaris cameras for 3D tracking of ultrasound. IMPACT lab is directed by H. Rivaz, a tenured Associate Professor at Concordia University, the Research Chair in Medical Image Analysis, Associate Editor of IEEE TMI and TUFFC, and a program committee (PC) member of MICCAI from 2017 to 2020 and 2022 to 2023. He was selected as a rising star in bio-imaging in Quebec by QBIN in 2022. He received his BSc from Sharif University of Technology, Master’s from UBC, and PhD from Johns Hopkins University, and a postdoctoral fellowship at MNI. His team has written several award-winning papers at the intersection of AI and ultrasound.About Dr. Afilalo’s lab
Dr. Afilalo is a cardiologist at the McGill University Health Center and a tenured associate professor. He holds a graduate degree in epidemiology from McGill University and level III training in advanced cardiac imaging from Harvard University. He has authored international guidelines in the fields of echocardiography and cardiovascular magnetic resonance imaging. He led multi-national cohort studies and clinical trials in the fields of frailty and sarcopenia for older adults with cardiovascular disease, publishing in journals such as JAMA Cardiology and JACC. His team has developed deep learning software (coreslicer.com) for clinicians to automate measurements of frailty and body composition from CT, MRI, and U/S images. These contributions have been recognized by awards from the Royal College, Canadian Cardiovascular Society, and American College of Cardiology. He is founding director of Canada’s first fellowship program in Geriatric Cardiology.Diversity Statement
Concordia University is committed to Employment Equity and encourages applications from women, Aboriginal Peoples, visible minorities, ethnic minorities, and persons with disabilities.Horizon Postdoctoral Fellowship in Machine Learning and Deep Learning in Ultrasound Imaging
Dr. Hassan Rivaz and Dr. Jonathan Afilalo are currently accepting applications for a postdoctoral position at the intersection of ultrasound imaging and machine learning. The position is funded through the prestigious HORIZON Postdoctoral Fellowship.
Minimum Required Qualifications
- PhD in ECE, CS, BME or a relevant field
- Strong background in one of these fields: Deep learning, ultrasound imaging, medical imaging, computer vision, machine learning
- Proven academic track record of publications in medical image analysis
Submission Process
Send to Drs. Rivaz (hrivaz[at sign]gmail.com) and Afilalo (jonathan.afilalo[at sign]mcgill.ca):- Curriculum Vitae (maximum 5 pages)
- One to three sample publications