- Enhance the team’s technology ability on machine learning (ML), deep learning (DL), computer vision (CV) and medical image processing.
- Provide project service, help to solve the technique issues encountered during the project and instruct on algorithm model improvement.
- Consultancy on the medical image computer aided diagnosis (CAD) related data augmentation, generalized deep learning model design, medical image analysis etc. industry direction and academic research scope.
- Bi-weekly meeting with the team.
- 1st Project Milestone
- Provide at least 2 technical lectures related to intelligent medical image analysis:
- Technology trend on machine learning/deep learning for medical image analysis
- A systematic review on data argumentation techniques
- Offer guidance and advice to the DL/ML algorithm design, which includes:
- Generative Adversarial Network (GAN) model design to address data imbalance and sparsity issue for endoscopic image analysis.
- Self-Supervised Learning (SSL) methods to solve the problem of low quality and low quantity of data labelling.
- Generalized DL model design for endoscopic image analysis.
- 2nd Project Milestone
- Provide at least 1 technical lecture related to intelligent medical image analysis:
- A systematic review on the self- and semi-supervised learning
- Review the algorithm models and results, instruct on the performance improvement, which includes:
- Lesion detection and early gastric cancer identification in endoscopic image
- Endoscopic image anatomical site classification
- Contribute one innovative idea in the field of AI medical image analysis.
- Ph.D. Degree in Computer Science or related disciplines
- 5+ years of experience in ML/DL algorithm research or computer vision related research in the field of medical image analysis.
- Both industry and academic research experience are highly preferred; can understand the requirement, formulate the problem, and propose innovative solutions addressing the problem effectively
- Good understanding and appreciation of algorithmic level fine tuning versus system-level performance optimization – various trade-off, parameters, adjustment, and modelling effects etc.
- Broad knowledge and connection in the community – keep abreast of the state of art of the latest progress, other industry players approach and academic R&D direction.
Interested candidates please send application (quoting Ref. No.) with detailed resume, current and expected salary to Talent Acquisition via email to [email protected]
Only short-listed candidates will be notified. ASTRI reserves the right not to fill the position.
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