[MIT의 Ericmoore Jossou 교수님 연구실 박사후 연구원 모집 안내]

- 지원 방법 : CV 첨부와 함께 직접 이메일 (ejossou@mit.edu) 로 지원

* 세부 내용은 아래 첨부된 영문 내용 읽어주시면 되며,

모집 공고 마감 등 상세 사항 문의는 위 이메일 주소로 정중히 보내시면 됩니다. 

Project description

X-ray computed tomography (XCT) is an imaging technique that uses X-ray radiation to generate three-dimensional images of internal object structures. XCT is used to detect a wide variety of surface-connected and internal discontinuities and flaws in materials, such as density variations, cracks, cavities, voids, porosity and micro/nanoporosity, laps, inclusions, foreign objects, impurities, segregation, bond separation, missing parts, part misalignment, and service degradation, as well as thickness and dimensional measurement, and flaw distribution[1].

However, XCT is a relatively slow and costly technique due to the large number of projections (radiographs from different angle positions) required to provide a high-quality volume reconstruction. Additionally, noise and artifacts are generated in the images when measuring materials with high X-ray absorption, such uranium containing alloys. The common data limitations during data acquisition include (i) acquiring only a limited number of projections (ii) noise in the projection images (ii) acquiring projections for a limited angular range.

Therefore, we seek a postdoctoral candidate who will focus on synchrotron based XCT data collection and analysis. The data analysis efforts will include developing and implementing artificial intelligence-based tools to enhance and accelerate XCT pre- and post-processing. They should be willing to travel to synchrotron light sources to conduct targeted ex-situ, in-situ, and operando tomography studies.

Requirements

The candidate is expected to have a solid background in programming – preferably python, artificial intelligence, and tomographic data processing. Knowledge of physical metallurgy is desirable but not compulsory.