Research Scientist – Computer Vision
Company Name:-
Adobe
Job Location:-
Noida, Uttar Pradesh
Job Summary:-
Our Company
Changing the world through digital experiences is what Adobes all about.
We give everyonefrom emerging artists to global brandseverything they need to design and deliver exceptional digital experiences! Were passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen.
Were on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity.
We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours!The Opportunity
Adobe Digital Experience Clouds mission is to transform how businesses compete through next generation digital experiences.
At the Media and Data Science Research (MDSR) lab, we work on fundamental and applied research problems relevant to the Experience Cloud.
Our research areas span Computer Vision, Natural Language Understanding and Data Science.
We also work on foundational questions related to AI and allied mathematical areas.
We publish at top machine learning conferences and also work with the product teams to turn our research into impactful product features.
We are looking for a Computer Vision researcher with a strong track record of publications in top tier conferences to define and lead research areas that will transform visual experiences for the customers of Adobes Experience Cloud.
Job responsibilities include developing and leading research areas, publishing in top conferences and journals, working with product teams to turn the research advances to impactful product features and to collaborate with academic labs.
Desired Qualifications and Skills
Multiple publications in top tier conferences such as CVPR, ICCV, ICLR, ECCV, NeurIPS, ICML etc.
Deep expertise in one or more of the following areas – Image and Video Understanding, Manipulation and Synthesis, Unsupervised Learning, Image and Video Segmentation, Generative Models like GANs, Normalizing Flows, Transfer Learning, Few Shot/Zero Shot Learning, 3D computer vision, Multi-Modal content and Document Understanding, Deep Reinforcement Learning, Graph Neural Net
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