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Neuromorphic Computing Research Jobs in Delaware

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What is neuromorphic computing research?

Neuromorphic computing research is the study and development of computer systems inspired by the structure and function of the human brain. Researchers in this field design hardware and software that mimic neural architectures, aiming to achieve greater efficiency and adaptability than traditional computing methods. This research often involves creating artificial neurons and synapses using novel materials and architectures to enable advanced tasks like pattern recognition and real-time learning. The ultimate goal is to create energy-efficient, intelligent computing systems for applications ranging from robotics to AI and sensory processing.

What are the key skills and qualifications needed to thrive in neuromorphic computing research?

To thrive in Neuromorphic Computing Research, a strong background in computer science, electrical engineering, neuroscience, and mathematics—often at the graduate level—is essential. Familiarity with programming languages (such as Python or C++), neural network frameworks, hardware description languages, and simulation tools like SpiNNaker or NEST, as well as published research experience, is typically required. Critical thinking, creativity, interdisciplinary collaboration, and effective communication set outstanding researchers apart in this evolving field. These skills and qualities are crucial for driving innovation and bridging the gap between biological intelligence and artificial computing systems.

What are some common challenges faced by professionals working in neuromorphic computing research?

Professionals in neuromorphic computing research often encounter challenges related to the interdisciplinary nature of the field, requiring deep knowledge of neuroscience, computer engineering, and machine learning. Developing hardware that effectively mimics neural architectures can be complex due to limitations in current fabrication technologies and the need for novel algorithms. Additionally, researchers must frequently collaborate with teams from diverse backgrounds, which necessitates strong communication and adaptability. Securing funding and staying updated with rapid advancements in both neuroscience and AI are also ongoing challenges in this dynamic research area.

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Neuromorphic Computing Research jobs near you

Post Doctoral Researcher, Electrical and Computer Engineering

The Chronicle Of Higher Education, Inc.

Newark, DE • On-site

$60 - $80/hr

Other

Posted 6 days ago

Job description

Context of Job:

As a Post-Doctoral Researcher, you will support Prof. Yuping Zeng with the frontier of analog circuit design, implementation and characterization, preferably for neuromorphic computing with emerging devices.

Major Responsibilities:
  • Plays a key role in the design of analog circuit with emerging devices developed in the group;
  • Deep understanding of the device physics and the know-how of circuit design;
  • Device fabrication, characterization, model extraction and apply device model in the circuit design;
  • Circuit design optimization;
  • Using Cadence for circuit design and ADS for RF circuit design;
  • Circuit implementation and characterization;
  • Plays a key role in helping and guiding graduate students.

You can select any of the following research areas that you feel competent on:

  • Devices: (1) High-frequency electronic devices: heterojunction bipolar transistors, high electron mobility transistors (HEMTs); (2) Low-power devices: novel transistors (tunneling field-effect transistors, and Non-silicon metal-oxide-semiconductor field-effect transistors, thin film transistors); (3) Light-emitting devices and high-speed detectors
  • Circuits: Analog circuits for Neuromorphic computing
Qualifications:
  • PhD required at the time of hire
  • 1-year analog circuit design experience preferred
  • Know how to design circuit using one or more of the software listed (Cadence; Fluent use of LT-Spice, Verilog-A, Python, ADS, ICCAP, etc.)
  • Know how to perform circuit analysis and characterization
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