Opportunities
PostDoctoral and Graduate Student Research Fellows XXX
Multiple postdoctoral research and PhD graduate research fellows are available in Yong Chen’s group in the School of Engineering and Applied Science and California NanoSystems Institute (CNSI) at University of California, Los Angeles (UCLA).
Qualifications:
Ph.D. and B.S. with excellent academic achievements in the field of electrical engineering, materials science, computer science, mechanical engineering, chemical engineering, chemistry with research experiences in electronic device fabrication, nanofabrication and nanocharacterization, circuit design and simulation, neuromorphic circuit and simulation, statistic modeling, feedback control, electronic polymers, carbon nanotube/polymer composites.
Project backgrounds:
The goal of the project is to establish a universal physical intelligence theoretical model that explains how self-organized structures with intelligent functions spontaneously evolve in a complex system and invent electronic neural circuits based on the theoretical model. The theory will unify a number of innovative theories in the fields of statistical mechanics, thermodynamics, complex systems, game control theory, and critical phenomena, etc. The theory will be implemented in a Self-Organized Physical Intelligence System (SOPHIS) with two interacting sub-systems: a physical intelligence unit (PIU) based on electronic neural circuits and a complex system. The project is led by Prof. Yong Chen at UCLA and supported by Defense Advanced Research Projects Agency.
Over six decades, modern electronics has evolved through a series of major developments (e.g., transistors, integrated circuits, memories, microprocessors) leading to the programmable electronic machines that are ubiquitous today. However, owing both to limitations in hardware and architecture, these machines are of limited utility in complex, real-world environments, which demand an intelligence that has not yet been captured in an algorithmic-computational paradigm. As compared to biological systems for example, today's programmable machines are less efficient by a factor of one million to one billion in complex, real-world environments. We plan to create a new materials, devices and circuits that can break the programmable machine paradigm and define a new path forward for creating useful, intelligent systems.
A neurological system requires a tremendous amount of synapses (an estimated 10^14 in the human brain) for signal processing, memory, and learning. Si electronic circuits and/or programmable computers have had very limited success to date in the field of neurological control systems for one primary reason: the lack of low-cost and low energy consumption circuits that can emulate the essential properties of synapses. We are developing devices that can have the functions of synapses and are integrated with conventional Si MOS transistors. The devices can be reconfigured flexibly in analog mode by input signals. It integrates logic, memory, and learning functions that can fully emulate biologic synapses while only occupying a tiny fraction of the size of the smallest CMOS representation of a synapse; moreover it is a nonvolatile passive device and thus requires little power to operate.
We plan to explore electronic configurable ionic conductive materials, such as conjugated polymer and carbon nanotube based composites to achieve desired synaptic characteristics. The neural circuits will be built on a large number (>10^6) of low-power consumption, low-cost, and high-speed synaptic devices integrated with CMOS circuits, which enables the circuits to dynamically process signals from a large amount of distributed sensors (>10^8) in parallel, and make real-time dynamic interactions and decisions to reconfigure the sensors and actuators. We will design the circuits with dynamic reconfiguration and adaptation capabilities similar to biological neuronal circuit architectures. The sensory information from the sensors will be spatially mapped to the collective model in the circuits and aggregate locally to produce a topographical mapping of the sensory information. A multi-layer cortical network will provide the necessary processing functions and ultimately result in a robust control strategy for the actuator system.
Conditions:
The University of California at Los Angeles (UCLA) is an exceptional research institution and an attractive and comfortable campus located in West Los Angeles. The salary is competitive, and medical insurance is included.
Biomedical Lab Technician
One technician research position is available in DNA detection/assay areas in Yong Chen's group which is physically located at both the California NanoSystems Institute (CNSI) as well as at the Henry Samueli School of Engineering and Applied Science, both at University Of California, Los Angeles (UCLA).
Project goal:
The overall goal of the project is to develop an ultrasensitive, reliable, rapid, simple, and economically feasible single-molecule biomarker detection platform for Mycobacterium tuberculosis (mTB) diagnosis. We plan to develop a platform that can detect mTB biomarkers (such as DNA) with an ultrahigh sensitivity down to single molecule level without PCR. The platform can detect mTB DNA biomarkers from the sputum samples of infected human patients.
Qualifications:
B.S. in biochemistry, medicine, chemical engineering, biophysics, chemistry, with research experiences in bioassay, DNA technology, and/or bionanotechnology. Knowledge with biochemistry, bioconjugation, nanoparticle synthesis, optical biodetection would also be an asset.
Conditions:
The University of California at Los Angeles (UCLA) is an exceptional research institution and an attractive and comfortable campus located in West Los Angeles. The salary is competitive and includes a medical insurance program.