Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
with implementing these algorithms on fully error-corrected fault-tolerant quantum computers. About You The incumbent is expected to have relevant experience in developing quantum architectures, error
-
Processing and Machine Learning to develop signal processing and machine learning algorithms and methods for communication networks. Key Responsibilities: Develop signal processing and machine learning
-
University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 14 hours ago
: CSC409H5F Scalable Computing (emergency post) Course description: We investigate computation in the large -- utilizing many CPUs with large amounts of memory, large storage and massive connectivity
-
optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. Become a part of our team and join us on our journey of research and innovation! What
-
to the development of smart AIoT prototype for monitoring. Key Responsibilities: Build and test AIoT prototype Develop and deploy self-navigation algorithms. Publish several papers in top journals. Write proposals
-
annotation and preprocessing. Research and develop algorithms and AI models for various computer vision tasks of PCB image analysis. Algorithm/model evaluation and testing. Maintain the integrity of the data
-
Join Oxford Digital Health Labs as a Senior Research Scientist or Software Engineer, where you will play a pivotal role in developing advanced algorithms for cardiotocography and fetal ECG analysis
-
meaning from data. Designing machine learning algorithms and undertaking testing for: predictive maintenance. operational performance and cost optimization. failure prevention and timely reparation of Aids
-
. Uses appropriate algorithms, logic, and data structures to solve problems and promote code reusability 4. Writes well organized, documented, and readable code 5. Uses appropriate revision control
-
collected and writing up experiment results in the field of computational biology. Core responsibilities include: Working with biological data - implementing algorithms, programming and data analysis