MERL is looking for research interns to conduct research into building and training novel architectures for small (~1 billion parameters) vision + language models. Interesting research directions include (a) diffusion and flow matching-based architectures, (b) architectures for improved visual reasoning, and (c) reducing confabulation using information-theoretic principles.
Prior experience with machine learning/computer vision/natural language processing research, and proficiency in building and experimenting with machine learning models using a framework like PyTorch are required. Candidates well into their PhD program with publications in top-tier machine learning, natural language processing or computer vision venues, ideally connected to building generative models, are strongly preferred. Candidates are also expected to collaborate with MERL researchers for preparing manuscripts for scientific publications based on the results obtained during the internship. Duration of the internship is 3 months with a flexible start date.
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