Research Intern - Computer Vision and Deep Learning

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. We are looking for a computer vision Research Intern with expertise in deep learning techniques to help our devices compute better understanding of the user and the environment. The ability to analyze multimodal sensor data and interpret various human and human-object interactions is key to Applied Sciences' mission of enabling a seamless set of human computer interactions. As part of this team, you will be working with a growing team of talented researchers already dedicated to this mission and use data and hardware only available to a select few. Naturally, the opportunity for you to push the state of the art in this field is huge. Currently enrolled in a Master or PhD program in Computer Science or a related STEM field. 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. Demonstrated publication record in top-tier vision or ML conferences (CVPR, NeurIPS, ICML, ICCV, ECCV, AAAI, IJCAI, 3DV) and journals (IEEE/ACM Transactions, IJCV). Comprehensive Deep Learning, algorithmic and software engineering background. Experienced in video-focused computer vision tasks, e.g. frame interpolation, video super-resolution, and video frame generation.

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