Fully Funded Master’s Student Position at SOLID Lab, Florida International University

Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab)
Knight Foundation School of Computing and Information Sciences
Florida International University
Position Start Date: As early as Summer 2024
Application Deadline: April 10, 2024
Position Description:
We are excited to offer a fully funded master’s student position within the Knight Foundation School of Computing and Information Sciences at Florida International University. The selected candidate will have the unique opportunity to engage in cutting-edge externally funded research projects, focusing on innovative problems in the field of deep learning, computer vision, and their applications.
Required Qualifications:
• A strong passion for exploring new challenges in deep learning and its applications.
• A bachelor’s degree with a strong academic record, preferably in Computer Science, Engineering, IT, or a related field.
• Demonstrated ability to work independently as well as part of a collaborative research team.
Preferred Qualifications:
• Practical experience with Python programming and deep learning frameworks such as PyTorch and TensorFlow.
• Previous experience in writing research papers and project reports.
• A foundational understanding of computer vision tools and their application in real-world scenarios.
Funding:
The position is fully funded, including a tuition waiver and a competitive stipend based on FIU rates. Additional funding opportunities for conference travel and publication fees are available.
How to Apply:
Interested candidates should submit the following documents:
• A detailed CV highlighting relevant experience.
• A 1-page cover letter expressing your interest and fit for the position.
• Transcripts from undergraduate education.
• Transcripts from Masters education (if you are currently enrolled in FIU KFSCIS master programs).
Please send your application package to solidlabnetwork@gmail.com by April 10, 2024.
For more information about the research group and projects, please visit www.solidlab.network

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