Research Intern - Neural Rendering & Character AI (Applied Science)

Research Interns put inquiry and theory into practice. Alongside fellow doctoral candidates and some of the world's best researchers, Research Interns learn, collaborate, and network for life. Research Interns not only advance their own careers, but they also contribute to exciting research and development strides. During the 12-week internship, Research Interns are paired with mentors and expected to collaborate with other Research Interns and researchers, present findings, and contribute to the vibrant life of the community. Research internships are available in all areas of research, and are offered year-round, though they typically begin in the summer. Conduct original research and build prototypes in areas such as: Neural rendering & scene representations (radiance fields, Gaussian splats, hybrid implicit/explicit models, dynamic/deformable fields). Generative 3D/4D (image-/video-/text-conditioned 3D generation, diffusion/score-based methods, consistency & editability). Digital human & facial animation (speech-/audio-driven animation, identity/expression control, temporal coherence). Interactive character intelligence (learned controllers, multimodal conditioning, behavior/style control). Design datasets and evaluation protocols; perform ablations and benchmark against public and internally curated evaluations. Currently enrolled in a Master's or Ph.D. program in Computer Science, Electrical Engineering, Applied Math, or related field with a focus in vision/graphics/machine learning. In addition to the qualifications below, you'll need to submit a minimum of two reference letters for this position as well as a cover letter and any relevant work or research samples. After you submit your application, a request for letters may be sent to your list of references on your behalf. Note that reference letters cannot be requested until after you have submitted your application, and furthermore, that they might not be automatically requested for all candidates. You may wish to alert your letter writers in advance, so they will be ready to submit your letter. Hands-on experience with deep learning (e.g., PyTorch); familiarity with diffusion/transformers and training pipelines. Proficient programming skills (Python and/or C++) and clear technical communication. Publications in CVPR, ICCV, ECCV, SIGGRAPH, NeurIPS, ICML, ICLR, 3DV, AAAI, IJCAI, or journals (e.g., IJCV, IEEE/ACM Transactions). Depth in one or more: neural rendering, Gaussian splatting, NeRF-style models & variants, generative 3D/4D (diffusion), speech-/text-conditioned animation, character control/behavior modeling, multimodal learning, 3D consistency & evaluation. Experience with scalable training, dataset curation, and evaluation; efficiency techniques (distillation/quantization) are a plus.