Search for Research Engineer roles in Los Angeles, CA, specifically in neuroimaging, requiring a Master's degree in Biomedical Engineering with 5+ years of experience in machine learning and AI applications in neuroimaging.

Okay, I've searched for Research Engineer roles in Los Angeles, CA within the neuroimaging field, requiring a Master's degree in Biomedical Engineering and 5+ years of experience in machine learning and AI applications. I have found 34 positions for you.

Research Engineer

Los Angeles, CA

Neuroimaging

Master's

5 years + Experience

Can you help me to analyze the job description and company culture?

Sure. Here's the analysis:

Job Analysis:

The Research Engineer role at USC's Keck School of Medicine is fundamentally about advancing the understanding of neurodegenerative disorders through advanced computational methods and machine learning. The key responsibilities involve processing and analyzing neuroimaging data, integrating it with clinical and genetic information, and contributing to scientific knowledge through publishing research findings. A critical aspect of this role is the ability to work with large datasets, which requires a strong foundation in machine learning techniques, particularly as they pertain to biomedical imaging. Success in this position hinges on effective collaboration with colleagues and the ability to communicate complex concepts both in written formats for publications and orally for presentations. The candidate will likely face challenges related to data interpretation and visualization as well as navigating the intricate intersection of neuroscience and technology. A successful Research Engineer will not only produce high-quality analyses but also foster an environment of learning and knowledge dissemination within the team, underscoring both technical expertise and effective mentorship capabilities.

Company Analysis:

USC's Keck School of Medicine, particularly its Neuroimaging and Informatics Institute, operates at the forefront of research in brain structure and function, positioning itself as a leader in the field of neuroimaging. This environment fosters innovation and multidisciplinary collaboration, essential for tackling complex research questions in neurology and psychiatry. The culture likely values cutting-edge research, collaborative problem-solving, and a commitment to using technology to enhance human understanding of the brain. This ethos is reflected in the job's focus on ongoing learning and professional development, as indicated by requirements for training others and publishing research. The Research Engineer role holds significant visibility and influence, given its crucial contributions to key projects that align with the university's mission to address pressing neurological issues. By working on neurodegenerative disorder projects, this position supports the larger academic and healthcare objectives of translating research into practical applications, thereby contributing to education and public health advancements.

Mock up some interview questions based on the job description.

Absolutely. Here are some mock interview questions that could come up:

1. Tell me about a time when you had to analyze a large dataset. What methods did you use and what challenges did you face?
1. Describe an experience where you communicated complex technical information to a non-technical audience. How did you ensure they understood?
1. Can you give an example of a project where you employed machine learning techniques for a research problem? What were the outcomes?
1. Have you ever supervised or trained team members on a project? How did you approach that responsibility?
1. How would you approach developing a machine learning pipeline for predicting brain decline in neuroimaging data?
1. What strategies would you employ to ensure the models you build for neuroimaging applications are both accurate and interpretable?
1. Imagine you are faced with incomplete neuroimaging data from a study. How would you handle this situation to continue your research?
1. Suppose you encounter conflicting results during your analysis. What steps would you take to resolve this discrepancy?
1. Why are you interested in the Research Engineer position at the University of Southern California and what do you hope to achieve here?
1. How do you see your expertise aligning with the mission of the Mark and Mary Stevens Neuroimaging and Informatics Institute?
1. What questions do you have about the collaborative opportunities within the department for future research projects?
1. Can you share your perspective on the importance of transparency in reporting results and methodologies in scientific research?
1. What specific tools or frameworks do you prefer for data visualization and why?
1. How do you stay up-to-date with the latest advancements in machine learning and its applications to neuroimaging?
1. Are there particular conferences or journals where you would like to present your work? What draws you to them?

Job Details

Overview Company

Research Engineer

$89.3k/y-$99k/y

Lensa

23 days ago

Highlight

OnsiteFull-time3+ yearsMid

Summary

University of Southern California is seeking a Research Engineer to join the Keck School of Medicine in Los Angeles, CA. The role involves working on neurodegenerative disorder projects, focusing on data processing, machine learning, and neuroimaging.

About the Role

As a Research Engineer, you will design, develop, and test materials and prototypes for research projects. You will work with large-scale databases to identify brain decline trajectories, apply machine learning and generative AI methods, and assist in preparing manuscripts and conference papers. You will also provide thorough documentation and train coworkers in methodologies.

About You

Required:

Master’s Degree in Electrical Engineering, Biomedical Engineering, Computer Science, or Data Science.
3+ years of experience in neuroimaging.
Strong understanding of machine learning, deep learning for biomedical imaging, and computer vision.
Experience with large-scale neuroimaging and genetics data.
Proficiency in oral and written communication.
Experience in designing and maintaining pipelines/tools using machine learning for neuroimaging applications.

Preferred:

5+ years of experience in AI applications in neuroimaging.
Experience with aging brain projects and MR image quality assessment tools.
Experience with synthetic data generation using generative models.
Knowledge of ML models for classification and regression, and deep learning networks like CNNs, RNNs, and GANs.
Experience with ML frameworks such as PyTorch, Tensorflow, Keras.
Contribution to AI in medical imaging conferences.

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Los Angeles, CA