Deep Learning Researcher (Sound Processing & Sensing with TinyML Focus)

We are on the lookout for a Deep Learning Researcher with a specialized focus on sound processing and sensing using TinyML.

This role is pivotal in developing efficient, lowpower sound processing algorithms that can operate on tiny, resource-constrained devices.

Key Responsibilities

Research, develop, and optimize deep learning models for sound processing tasks such as noise suppression, automatic speech recognition, keyword spotting, biometric verification, text to speech, sound event detection. The primary focus will be on TinyML applications.
Research and implement state-of-the-art model compression and quantization techniques to ensure models are efficient and effective on low-power, low-resource devices.
Collaborate with software engineers to deploy optimized models on microcontrollers and embedded systems, ensuring seamless integration and performance.
Conduct rigorous testing and validation of models to guarantee robustness and reliability in real-world applications. • Stay abreast of the latest advancements in deep learning, model compression, quantization, and TinyML technologies, applying this knowledge to ongoing projects.
Publish research findings in leading scientific journals and present at international conferences to contribute to the broader scientific community

Requirements:

The ideal candidate will possess proven experience in implementing, training, and deploying TinyML deep learning models, with a strong emphasis on model compression and quantization techniques.

PhD in Computer Science, Electrical Engineering, or a related field, with a focus on deep learning, signal processing, or acoustics.
Minimum of two years’ experience as a researcher: Demonstrated experience in developing, training, and deploying TinyML models, with a strong focus on sound processing and sensing applications.
Strong background in model compression and quantization techniques, with a proven track record of optimizing deep learning models for embedded systems.
Proficiency in programming languages such as Python, and experience with deep learning frameworks like TensorFlow or PyTorch. • Excellent analytical and problem-solving skills, with the ability to work independently and as part of a multidisciplinary team. Preferred Skills
Hands-on experience with TensorFlow Lite for Microcontrollers and other TinyML platforms
Knowledge of audio signal processing techniques and familiarity with sound sensing technologies
Experience with development of AI models for resource-constrained environments.