We are seeking a highly motivated and innovative Research Scientist with a strong background in statistics, signal processing, and information theory to join our team focused on solving complex problems at the intersection of algorithm design, neural network optimization, and edge AI. You will play a key role in developing cutting-edge algorithms for NP-hard combinatorial problems, compressing and optimizing neural networks for resource-constrained edge devices. This is a unique opportunity to contribute to both theoretical advancements and real-world applications of AI.
Responsibilities
Develop novel techniques for neural network quantization, pruning, and compression to enable efficient deployment on edge devices.
Design and analyze algorithms for NP-hard combinatorial optimization problems, with a focus on applications in AI and machine learning.
Collaborate with engineering and software teams to integrate research findings into product.
Stay abreast of the latest research in relevant fields and publish findings in top-tier academic conferences and journals.
Requirements:
Must hold a PhD in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.
Demonstrated expertise in one of the following: statistics and random signal, signal processing, and information theory.
Strong programming skills in python, and Matlab.
Experience with deep learning frameworks (e.g., PyTorch, TensorFlow).
Strong publication record in top-tier academic venues, such as CVPR, ECCV, NIPS, ICASSP, InterSpeech, etc.
Experience in theoretical and empirical research and in addressing research problems.
Experience in combinatorial and non-convex optimizations.
Experience communicating research for public audiences of peers.
Familiarity with embedded systems and edge computing platforms is a plus.
Must hold a PhD in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.
Demonstrated expertise in one of the following: statistics and random signal, signal processing, and information theory.
Strong programming skills in python, and Matlab.
Experience with deep learning frameworks (e.g., PyTorch, TensorFlow).
Strong publication record in top-tier academic venues, such as CVPR, ECCV, NIPS, ICASSP, InterSpeech, etc.
Experience in theoretical and empirical research and in addressing research problems.
Experience in combinatorial and non-convex optimizations.
Experience communicating research for public audiences of peers.
Familiarity with embedded systems and edge computing platforms is a plus.
This position is open to all candidates.
