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Research Engineer-Automatic Speech Recognition, Shanghai, China
Overview:
Research Engineer-Voice Biometrics (VB) / Speaker Recognition (SR), Shanghai, China
Overview:
Research Engineer-Natural Language Processing, Shanghai, China
Overview:
Overview: Nuance Automotive specializes in conversational AI technologies for car manufacturers, helping them deliver unique user experiences to their customers. With the Dragon Drive platform, Nuance offers a deeply integrated hybrid solution that can be customized to become an OEM-branded smart automotive assistant which seamlessly integrates into the user’s connected ecosystem.
In this position, you will pursue cutting edge research aligned with Nuance’s long-term AI roadmap. The focus will be on developing or improving deep learning algorithms/models for application in natural language understanding and conversational AI systems.
We are looking for a research engineer in the area of Machine Learning and Deep Neural Networks. You will join our core research team in the Automotive department and help us push the state of the art in Natural Language Processing and related fields, such as Question Answering and Semantic Parsing. You will contribute to the next generation of our in-car voice user interface and virtual assistant.
This position is located in our office in Aachen, Germany.
We are looking for an excellent Senior Research Scientist in the area of Machine Learning and Deep Neural Networks. You will join our core research team in the Automotive department and help us push the state of the art in Natural Language Processing and related fields, such as Emotion Recognition, Question Answering, and Semantic Parsing. You will contribute to the next generation of our in-car voice user interface and virtual assistant.
You will contribute to the next generations of automatic speech recognition (ASR) for automotive applications on embedded platforms and on the cloud. As senior research engineer you will investigate and implement speech recognition algorithms focusing on the integration of the newest deep learning based techniques targetting continuously listening applications.
As a Research Application Scientist you will support us in the R&D in natural language understanding (NLU) for Japanese. You will assist in all aspects of planning, developing and maintaining high-quality NLU systems for customers. The job includes new feature development, accuracy improvement, project maintenance, and language-targeted research for improvements.
The position can be filled in the following office locations: Aachen (Germany) or in Burlington, MA (US)
As a Principal Research Scientist you will do research into all aspects of speech recognition algorithms with the aim of optimizing accuracy. This will include research into algorithms to increase robustness to various acoustic conditions encountered in real-world data, as well as underlying modelling techniques.
This position can be filled in our office in Cambridge, UK.
Responsibilities:
The candidate is expected to contribute to cutting edge research in Artificial Intelligence and Machine Learning applications.
It is well known that the convergence of Gaussian belief propagation (BP) is not guaranteed in loopy graphs. The classical convergence conditions, including diagonal dominance, walk-summability, and convex decomposition, are derived under pairwise factorizations of the joint Gaussian distribution. However, many applications run Gaussian BP under high-order factorizations, making the classical results not applicable.
Spike estimation from calcium (Ca
This paper introduces a node-asynchronous communication protocol in which an agent in a network wakes up randomly and independently, collects states of its neighbors, updates its own state, and then broadcasts back to its neighbors. This protocol differs from consensus algorithms and it allows distributed computation of an arbitrary eigenvector of the network, in which communication between agents is allowed to be directed.
In this paper, we study the problem of recovering a group sparse vector from a small number of linear measurements. In the past, the common approach has been to use various “group sparsity-inducing” norms such as the Group LASSO norm for this purpose. By using the theory of convex relaxations, we show that it is also possible to use 1 -norm minimization for group sparse recovery.
Lecture Date: June 2, 2019
Chapter: Toronto
Chapter Chair: Mehnaz Shokrollahi
Topic: Hyperspectral Unmixing: Insights and Beyond
Lecture Date: May 31, 2019
Chapter: Oregon
Chapter Chair: Jinsub Kim
Topic: Hyperspectral Unmixing: Insights and Beyond