美國休斯頓大學(xué)2024年招聘博士后職位（定量多模態(tài)醫(yī)學(xué)成像）
休斯敦大學(xué)（University of Houston），簡稱UH，屬于研究型大學(xué)。該校學(xué)科涵蓋以下領(lǐng)域：自然科學(xué)、經(jīng)濟(jì)、法律、工程學(xué)、管理學(xué)、環(huán)境、建筑、教育、旅游、農(nóng)林、藝術(shù)、體育、新聞傳播、信息科學(xué)、醫(yī)學(xué)、語言等。休斯敦大學(xué)是美國得克薩斯州休斯敦的公立大學(xué)，位于休斯敦市中心東南區(qū)。在1927年3月7日創(chuàng)立，擁有近44000名學(xué)生，是得克薩斯州的第三大學(xué)府�？▋�(nèi)基學(xué)術(shù)基金會(huì)將UH評(píng)為“具有最高研究活動(dòng)的博士學(xué)位授予機(jī)構(gòu)”。2021年QS美國大學(xué)排名中排名第66位。
Postdoctoral/Senior Research Scientist-X-ray, photon counting, CT, phase contrast, PET, SPECT, Image Science, Psychophysics, Image quality, Multimodality
University of Houston
Job Description
PhD and Postdoctoral positions (multi-year positions with opportunities for career development to be independent investigators) are available in the Das Laboratory Candidates from all areas of science and engineering with relevant experience are welcome to apply. Contact Prof. Das (mdas@uh.edu) for additional information.
1) X-Ray CT or Optical Phase Contrast, Photon Counting Detectors, Spectral CT, Quantitative Multi-modality Imaging (PET/SPECT/US/MRI/Optical/Thermal)
We will develop the science and engineering principles to advance X-ray optics and imaging along with quantitative and multi-parametric information from multi-modality and advanced imaging systems.
NIH-funded projects on benchtop experimental/ prototype imaging system development and algorithms for advanced and multi-modality imaging. Work on system designs, physics models, computation, benchtop experiments with small animals, and clinical translation.
Experience preferred: X-ray CT, phase contrast and phase retrieval, photon counting detectors, image reconstruction methods, multimodality platforms (including any of the following: PET, SPECT, MRI, US, Optical, Thermal imaging), phantom or small animal imaging, quantitative imaging, imaging biomarkers, radiomics. Both experimental and
computational positions available.
Skills preferred include any of these: Monte Carlo simulations, benchtop and prototype imaging system design,
Excellent programming skills, Medipix/Timepix detectors, analytical models, forward, and inverse problems, and prototype system development for clinical translation.
2) Image Science, Psychophysics, Perception, Eye-Tracking, Image Texture
Computational methods assess image quality from the perspective of benefiting human observers' (like radiologists) ability to detect and classify signals in complex biomedical images. This includes eye tracking studies, working with simulated and clinical data, understanding optimal system design through task-based assessment (ROC, LROC studies), and image texture analysis using second-order statistical texture features. Our recent work also examines developing models for visual and optical illusions that might enhance signal detection in complex images.
Necessary skills: Programming, careful data analysis, high-performance computing, shell scripting (c, bash), medical imaging
Candidates with a recent PhD or those anticipating Ph.D. (all relevant areas of science and engineering) in the upcoming year are encouraged to apply for the postdoctoral positions. Opportunities exist for working with industrial and clinical collaborators as well as in developing creative and independent future career paths. Candidates are expected to work closely with graduate and undergraduate students. Those with extensive publications and research experience will also be considered for senior research positions. Please contact Prof. Mini Das (mdas@uh.edu) directly with your CV and briefly describe your research interests and goals in the email.
3) Collaborative Project and Joint Positions with Gifford Lab (UH)
We are collaborating with Dr. Howard Gifford in developing mathematical models for observer variability. For this collaborative project please contact Das (mdas@uh.edu) and Gifford (hgifford@uh.edu)
Description for Gifford R01: The position is funded by a new NIH R01 grant with the objective of developing low-resource statistical models that can reliably mimic the performance of expert readers in imaging trials involving complex target detection and estimation tasks. Models that can operate with relatively sparse training requirements would be useful in the evaluation of diagnostic imaging technology at all stages of development and application. Our project is principally focused on problems in radiography (CT, DBT) and nuclear medicine.
Funding support is available for multiple years for all levels and with a flexible start date. These positions offer competitive salaries and benefits. The salary range is based on qualifications, publications, and relevant experience. Our lab website is at https://sites.google.com/nsm.uh.edu/ipl/home .
