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Postdoctoral Position - Decision Making with Limited Data using Artificial Intelligence - An Active Inference Approach
Signals and Systems Division, Department of Electrical Engineering, Uppsala University has a vacancy for a postdoctoral position. The position is a part of the strategic research area effort eSSENCE´s PostDoc-program towards new e-science methods and tools for artificial intelligence in research.
Due to the recent situation we decided to extend the deadline for our open position on emotion recognition:
The Signal Processing (SP) research group at the Universität Hamburg in Germany is hiring a Research Associate / PhD student.
Section: Publications & Resources
Publication: IEEE Transactions on Computational Imaging
DUAL-energy computed tomography (DECT) differentiates materials by exploiting the varying material linear attenuation coefficients (LACs) for different x-ray energy spectra. Multi-material decomposition (MMD) is a particularly attractive DECT clinical application to distinguish the complicated material components within the human body.
Section: Publications & Resources
Publication: IEEE Transactions on Computational Imaging
The intrinsically limited spatial resolution of positron emission tomography (PET) confounds image quantitation. This paper presents an image deblurring and super-resolution framework for PET using anatomical guidance provided by high-resolution magnetic resonance (MR) images. The framework relies on image-domain postprocessing of already-reconstructed PET images by means of spatially variant deconvolution stabilized by an MR-based joint entropy penalty function.
Section: Publications & Resources
Publication: IEEE Transactions on Computational Imaging
The image blurring that results from moving a camera with the shutter open is normally regarded as undesirable. However, the blurring of the images encapsulates information that can be extracted to recover the light rays present within the scene. Given the correct recovery of the light rays that resulted in a blurred image, it is possible to reconstruct images...
The goal of the project is to design an architecture and self-learning algorithms that can optimize and guarantee deterministic QoS of individual traffic flows whilst maximizing network resource efficiency. The general QoS architecture for end-to-end communication of heterogeneous types of traffic will include routing and scheduling for each traffic type and admission control possibly with QoS renegotiations and with shaping and spacing.
Section: Events
Submission Deadline: February 29, 2020
Call for Proposals Document
Section: Events
Submission Deadline: January 24, 2020
Call for Proposals Document
