We're looking for a Deep Learning, Sensor Data Fusion Intern to join our Signify Research Team in Cambridge, MA. This role can either sit in Cambridge MA or work remotely from home.

Working for Signify means being creative and adaptive. Our culture of continuous learning and commitment to diversity and inclusion creates an environment that allows you to build your skills and career. Together, we're transforming our industry.

As the world leader in lighting, we're constantly ahead of the curve. Through our leadership in connected lighting and the Internet of Things, we're breaking new grounds in data analytics, AI, and smart homes, offices, cities and more!

Signify is one of the few companies in the world to achieve carbon neutrality and our next sustainability goals are even bolder: doubling our positive impact on the environment and society by 2025.

As a Deep Learning, Sensor Data Fusion Intern you'll be responsible for work in the area of machine learning for mobile platforms developing new algorithms on data from various sensors in the smart phones/watches to enable interactions with connected lighting system. Towards the end of the internship, you will be expected to share work in the form of a demo and technical report with other colleagues in the group. You will also be working in a team setting with scientists, taking initiatives, and contributing highly innovative ideas. Previous internships in our group have led to several publications and/or patent filings.

We're on the lookout for forward-thinking innovators with a passion for sustainability. If you match this description, get in touch!

As a Deep Learning, Sensor Data Fusion Intern, you will work from June 7th to August 20th. These are not fixed dates and can be adjusted. This internship could also lead to a full time at-will position.

What you'll do

• Build an IoT data analytics platform to connect people, lighting, and facility with machine learning algorithms.
• Design and develop machine learning algorithms with lighting IoT sensor data
• Explore deep learning and probabilistic approaches to fuse image and audio data
• Build a real-time demo to fuse data from different modalities for activity and event detection.

Your qualifications

• Must be able to work in the United States without corporate sponsorship now and within the future.
• A graduate-level student (PhD preferred) in computer science, engineering, or other related disciplines. A focus on Bayesian inference and machine learning is highly preferred.
• Good software engineering skills with Python is required. Knowledge with Java and/or C is a plus.
• Experience with machine/deep learning framework such as SKLearns and Keras. Knowledge with PyTorch, and/or TensorFlow is a plus.
• Experience with cognitive psychology, evidence-based design, human factors research
• Experience with database including SQL, NoSQL, RDS, etc.

• Pay Rate: Hourly Rate (minimum to maximum based on qualifications of the role)
  • EX: Hourly Rate ($29 up to $41 based on year of study)
• Benefits: Benefits Overview: Company subsidized benefits plan offerings that includes Medical and a Health Savings Account.

What you'll get in return...

• Hands-on experience on working with real-world IoT sensor-data
• Work with a diverse Research team to apply cutting-edge lighting solutions in a wide variety of applications
• Contribute to Intellectual Property (Patents and Trade-secrets) and publish in top peer-reviewed conferences and journals
• Opportunity to learn and apply techniques to real world problems.

Seethrough the eyes of our employees!

Who we are

Signify is the world leader in lighting. We provide professional customers and consumers with quality, energy-efficient LED lighting. And our lights, when connected, bring data to devices, apps and people - redefining what light can do and how you use it. Today, our innovations, such as LiFi - internet connectivity through light - and UV-C, solar and horticultural lighting, contribute to a safer, smarter, more sustainable world.

We're operating in 74 countries with 32,000 people worldwide. #LI-TR1