Graduate Intern: Interactive-Segmentation for X-ray Tomography (ML/AI)
PARC is offering a Research Internship opportunity in the areas of 3D image segmentation, machine learning, artificial intelligence, and human-in-the-loop methodologies, with a focus on practical skills. The candidate will have the opportunity to work with single-cell soft X-ray tomographic data produced at the National Center for X-ray Tomography (https://ncxt.org/; Lawrence Berkeley National Laboratory). They will gain experience in interpreting large biological tomographic images and develop semi-automatic methods to segment biological images with limited 3D features. Exceptional graduate students with hands-on experience in these fields are encouraged to apply.
Responsibilities:
• Develop and implement machine learning algorithms for 3D image segmentation, incorporating human-in-the-loop methodologies.
• Develop high-quality and scalable code according to industry standards.
• Contribute to conference or journal papers in the field of 3D image segmentation and machine learning, with an emphasis on human-in-the-loop techniques.
Minimum Requirements:
• Bachelor’s degree in Computer Science, Software/Computer/Electrical Engineering, Applied Math, or related discipline
•Demonstrable excellence in at least one high-level programming language (e.g., Python or C/C++, not notebooks); willingness to develop according to industry standards (development for deployment, clean and collaborative coding, git usage, unit testing, etc.)
• Eagerness to learn new technologies, apply practical techniques to solve real-world problems, and develop new ideas into software products.
• Up-to-date knowledge of machine learning, deep learning, and various neural network models and their pros and cons for different applications, particularly in the context of 3D image segmentation and human-in-the-loop methodologies.
• Outstanding written and oral communication skills.
Preferred Skills:
• At least one year of academic research experience as a Graduate/Research Assistant in an ongoing higher-education (Master’s or Ph.D.) program, focusing on 3D image segmentation, machine learning, and human-in-the-loop techniques.
• Experience using napari for visualization and annotation of multi-dimensional image data.
• Familiarity with popular deep learning frameworks, such as TensorFlow or PyTorch, and libraries for image processing, such as OpenCV or scikit-image.
• Strong problem-solving skills and the ability to work independently or collaboratively within a team.
• Knowledge of best practices in human-computer interaction, user experience, and/or user interface design for incorporating human feedback into segmentation tasks.
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