Computer Engineering
Computer Engineering
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The KINDI Center for Computing Research at the College of Engineering in Qatar University is seeking high-caliber candidates for a research faculty position at the level of assistant professor in the area of artificial intelligence (AI). The applicant should possess a Ph.D. degree in Computer Science or Computer Engineering or related fields from an internationally recognized university and should demonstrate an outstanding research record in AI and related subareas (e.g., machine/deep learning (ML/DL), computer vision, robotics, natural language processing, etc.) and fields (e.g., data science, big data analytics, etc.). Candidates with good hands-on experience are preferred. The position is available immediately.
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We seek to appoint a full-time Machine Learning Research Engineer to contribute to the development of new technologies for cutting edge vision systems in the context of an industrial project in collaboration with a large multinational company. The project carries out innovative research on the topics of Visual Question Answering and fast adaptation of vision-language models. The project team will be responsible for all the phases of the research development, including methods design and implementation, data preparation and benchmarking, task planning and frequent reporting. Within the team, your main responsibilities and duties will depend on your expertise and experience.
Poramate Manoonpong
The School of Information Science and Technology (IST) at Vidyasirimedhi Institute of Science and Technology (VISTEC), Thailand, offers a full-time position (Professor / Associate Professor / Assistant Professor / Lecturer (Tenure Track)). We are expanding our faculty to support the Sense-Think-Act initiative, which focuses on the next generation of AI-driven systems capable of perceiving their environment, reasoning effectively, and acting autonomously. We seek faculty members from diverse fields, including Computer Science, Computer Engineering, Software Engineering, Robotics, and Biomedical Engineering, to drive innovation in AI, robotics, and intelligent systems.
“Development and application of gaze control models for active perception”
Gaze shifts in humans serve to direct high-resolution vision provided by the fovea towards areas in the environment. Gaze can be considered a proxy for attention or indicator of the relative importance of different parts of the environment. In this talk, we discuss the development of generative models of human gaze in response to visual input. We discuss how such models can be learned, both using supervised learning and using implicit feedback as an agent interacts with the environment, the latter being more plausible in biological agents. We also discuss two ways such models can be used. First, they can be used to improve the performance of artificial autonomous systems, in applications such as autonomous navigation. Second, because these models are contingent on the human’s task, goals, and/or state in the context of the environment, observations of gaze can be used to infer information about user intent. This information can be used to improve human-machine and human robot interaction, by making interfaces more anticipative. We discuss example applications in gaze-typing, robotic tele-operation and human-robot interaction.
Computational Imaging: Augmenting Optics with Algorithms for Biomedical Microscopy and Neural Imaging
Computational imaging seeks to achieve novel capabilities and overcome conventional limitations by combining optics and algorithms. In this seminar, I will discuss two computational imaging technologies developed in Boston University Computational Imaging Systems lab, including Intensity Diffraction Tomography and Computational Miniature Mesoscope. In our intensity diffraction tomography system, we demonstrate 3D quantitative phase imaging on a simple LED array microscope. We develop both single-scattering and multiple-scattering models to image complex biological samples. In our Computational Miniature Mesoscope, we demonstrate single-shot 3D high-resolution fluorescence imaging across a wide field-of-view in a miniaturized platform. We develop methods to characterize 3D spatially varying aberrations and physical simulator-based deep learning strategies to achieve fast and accurate reconstructions. Broadly, I will discuss how synergies between novel optical instrumentation, physical modeling, and model- and learning-based computational algorithms can push the limits in biomedical microscopy and neural imaging.