Lab overview
The Data and Devices Lab at Nokia Bell Labs is a multi-disciplinary team of physicists, chemists, engineers, and computer scientists that explore novel devices and AI that will intimately couple humanity to the digital world. We work on both distributed physical – machine vision, chemical detection, air quality – and non-invasive physiological – biochemical, bioelectrical, and bio-optical – sensors from the physical layer to AI perception. We believe the intimate co-development of hardware and AI is key to enabling future ubiquitous, unobtrusive, multi-sensory systems that support industrial autonomization and the evolution of humanity towards Homo augmentus.
Join the world’s leading experts in information and communications technologies
We’re searching for creative and highly motivated technologists and researchers who share our mindset: curious, passionate and looking to make discoveries that will help change human existence for the better.
Bell Labs Machine Learning Researcher
Our team, Statistics and Data Science Research Group, is part of Bell Labs Artificial Intelligence Lab. We are statisticians, computer scientists, and engineers, designing and developing innovative solutions to advance the state-of-the art in AI/ML and data science. We have multiple openings for researchers with background in any of our research areas including, but not limited to, computer vision, time-series analytics, NLP, autoML, visual analytics, interactive machine learning, active learning, and deep learning.
Bell Labs - Research Statistician
We are looking for an individual who is a passionate problem-solver in data, Artificial Intelligence, and Machine Learning.
Bell Labs - Computer Vision Researcher
Locations
New Jersey, USA
Artificial Intelligence Research Lab (AIRL), Nokia Bell Labs, 600 Mountain Ave., New Providence New Jersey, USA
Cambridge, United Kingdom
Artificial Intelligence Research Lab (AIRL), Nokia Bell Labs, 21 JJ Thomson Ave., CB3 0FA, Cambridge, UK
APA style publications
- R. L. Willett, et al., “Interference Measurements of Non-Abelian e/4 & Abelian e/2 Quasiparticle Braiding,” Phys. Rev. X 13, 011028 (2023). (Link)
- L. Meegahapola, et al., “Quantified Canine: Inferring Dog Personality From Wearables,” in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). (Link)
- J. Stuchbury-Wass, et al., "Heart Rate Extraction from Abdominal Audio Signals," 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (Link)
- R. L. Willett, et al., “Atypical edge current interference in high quality 2D electron systems: both Ising and Fibonacci anyons?” APS March Meeting 2023. (Link)
