Job Description

Job Description

Intel's Neuromorphic Computing Lab is seeking talented neuromorphic algorithms researchers to pioneer a new class of algorithms targeting Intel's latest generation of neuromorphic processors. Our goal is to exploit neuromorphic and related non-von Neumann architectures to solve real-world problems with orders of magnitude gains versus the best known conventional solutions in applications ranging from cloud to edge based systems.

As a research scientist in this position you have the opportunity to shape the development of novel brain-inspired algorithms and applications as well as Intel's new open-source neuromorphic SW framework:

- In your primary role as an algorithm researcher, you will develop offline and online learning algorithms able to adapt models incrementally, in real-time from sequential data without suffering from catastrophic forgetting by drawing from your expertise in state-of-the-art machine and deep learning models or your understanding of neuroscientific principles. To accomplish these goals, you may leverage meta-learning or few shot learning methods, develop data-efficient un/self-supervised learning algorithms, incorporate anomaly detection or attention mechanisms to focus limited compute resources on the most salient aspects of a task or utilize other methods among the approaches you are familiar with.

- In your second role as a neuromorphic SW developer, you will develop reusable, application-oriented open-source SW libraries to demonstrate real-world use cases on Intel neuromorphic systems and to benchmark their performance against conventional solutions.

As part of our team, you have the unique opportunity to work with external researchers and industrial partners across the world participating in Intel's Neuromorphic Research Community and you will be able to publish your results in peer-reviewed journals or at conferences.

Your contributions in all of these areas will have a direct impact to enable breakthrough neuromorphic applications in the space of computer vision, natural language processing, autonomous vehicles, robotics, and large-scale scientific as well as big data problems in the years ahead.

Qualifications

Experience and knowledge listed below may be obtained through industry experience or education coursework:

Phd degree in Computer Science, Computational Neuroscience, Physics, Mathematics, Electrical/Computer Engineering, or a related field with a focus on machine learning or neuro-inspired computational algorithms and applications.

At least two years of experience developing novel algorithms and applications relating to deep (spiking) neural networks.

Solid fundamentals in using frameworks such as Tensorflow, PyTorch or other neuromorphic programming APIs.

Proven expertise in Python and/or C/C++ with the ability to design, write and test quality code, i.e. as demonstrated by contributions to github projects.

Strong grasp of computer science fundamentals and mathematics.

Inside this Business Group
Intel Labs is the company's world-class, industry leading research organization, responsible for driving Intel's technology pipeline and creating new opportunities. The mission of Intel Labs is to deliver breakthrough technologies to fuel Intel's growth. This includes identifying and exploring compelling new technologies and high risk opportunities ahead of business unit investment and demonstrating first-to-market technologies and innovative new usages for computing technology. Intel Labs engages the leading thinkers in academia and industry in addition to partnering closely with Intel business units.

Posting Statement

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.

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