OPENINGS FOR GRADUATE STUDENTS AND POST-DOCTORAL FELLOWS (as of Nov. 2017)

 

(MENG Projects and Undergraduate Summer / Thesis Projects openings in these areas are also available - additional openings are listed here)

 

Starting dates are Jan., May or Sept. 2018-2019, or on a rolling basis any time of the year. Most of the openings listed below are available immediately but not all will be filled. If you are looking to apply for a PhD degree or post-doctoral studies please contact me by email directly. Please attach your CV/resume (WITH GPA CLEARLY STATED) and your transcript, in pdf format. If you have received a NSERC or OGS graduate scholarship or have another funding source, please inform me of this. Positions are very competitively funded. Most positions are in ECE, in IBBME or can be cross-appointed through ECE-IBBME Collaborative program.

                                                                                                                    

1.    (NEW!) Computational Image Sensors and Cameras that can “See” through Skin and around the Corner (two positions)

 

     The main goal will be to study, design, and deploy a new class of computational cameras whose key property is that they are "transport-aware." Unlike conventional cameras which record all incident light, transport-aware cameras can be programmed to block some of that light, based on the actual 3D paths it followed through a scene. Transport-aware cameras use a programmable light source for illumination and a programmable sensor mask for imaging, and are a pioneering breakthrough in the field of computational photography, with diverse novel applications, as unimaginable as seeing around the corner and seeing through skin!

     Live video from a transport-aware camera can offer a very unconventional view of our everyday world in which refraction and scattering can be selectively blocked or enhanced, visual structures too subtle to notice with the naked eye can become apparent, and object surfaces can be reconstructed in 3D under challenging conditions well beyond the state of the art.

     These capabilities will find new uses in many industrial, scientific and commercial applications such as: augmented and virtual reality (for gesture recognition and object recognition), self-driving cars, 3D printers and scanners, video games, biomedical imaging systems (such as endoscopy), material analysis, drones, robots, and industrial machine vision.

     The applicants should have interest and ideally previous experience in some of the following areas:

a. CMOS analog and mixed-signal integrated circuits/systems design (e.g., CMOS imager circuits such as column-parallel amplifiers and ADCs), 

b. Understanding of device physics of photodetectors as well as their 3D structure photo-generated charge simulation and layout (e.g., pinned photodiode, photonic mixer device),

c. Embedded systems: computer architecture and microprocessor IP on-chip instantiation and programming (e.g., using open-core microprocessor IP such as TI MSP430 or ARM),

d. Embedded systems: solid knowledge of Verilog or other hardware description languages for designing top-level camera architectures (e.g., design and programming of high-date-rate I/O interfaces such as USB3, memory IP interfaces such as DDR2/3/4, FPGA programming tools such as by Xilinx or Altera).

     This is a highly collaborative project with several research groups around the world (USA, Italy), including those specializing in photodetector design, time-of-flight imaging, computer vision, and robotics. There may be opportunities to travel internationally to top research centers in North America and Europe.

 

 

2.    (NEW!) E-PHYS: Brain-Chip Interfaces/Neurostimulators for Diagnostics/Treatment of Neurological Disorders

Most of these projects involve implantable and/or wearable CMOS analog integrated circuits/systems design for neuro-electrical signal acquisition, filtering and amplification (electrophysiology), ADC/DAC design, on-chip signal processing, RF communication (transceivers), inductive powering, electrical neurostimulation, interfacing with brain-implanted high-count microelectrodes, integration/interfacing with on-chip and off-chip microelectrodes, and in vivo experimentation with animals.

 

2.1. Embedded systems design for wearable brain interfaces  (using commercially available IC components)

2.2. Implantable wireless integrated circuits for brain neural activity monitoring and modulation

2.3. Wireless brain implants with on-chip artificial intelligence  (including studying machine learning algorithms and mapping them onto integrated circuits for on-line data classification and pattern recognition in neural recordings – this is collaborative with our AI-oriented team members)

2.4.  Brain implants for optogenetic stimulation (using hybrid integration of electronics and photonic lasers, etc)

2.5.  Selected topics in neuroscience, neurology, neurosurgery, electrophysiology, animal neurosurgery, epileptology (experience or interest in electrical engineering and electronics is of benefit, but is not a must)

 

                                                                                                                                                                                                                                                                                                                                                                   

3.    (NEW!) E-CHEM:  Fully-Wireless Single-Chip Microsystems for Electrochemical Diagnostics and Therapy, both IN VIVO and IN VITRO

 

Most of these projects involve wearable / implantable / disposable CMOS analog integrated circuits/systems design for neuro-/bio-chemical signal acquisition methods such as voltammetry, amperometry and impedance spectroscopy, signal filtering and amplification, ADC/DAC design, on-chip signal processing, RF communication (transceivers), inductive powering, electrical neurostimulation, interfacing with brain-implanted high-count microelectrodes, integration/interfacing with on-chip and off-chip microelectrodes, and in vivo experimentation with animals.

 

3.1. Circuits and systems for electrochemical sensing, imaging and modulation of neurochemicals in the brain (please see our 2016 MDPI Sensors paper and ISSCC 2017 paper for more details).

3.2. Implantable chips for peripheral nerve interfaces and electroceuticals (e.g., responsive micro-stimulators)

3.3. Gas sensors and olfactory sensors for volatile organic compounds detection in human breath in order to perform early medical diagnostics

3.4. High-throughput drug screening chips - integrated circuits for patch-clamp electrophysiology

 

 

4.    (NEW!) Machine Learning ALGORITHMS For Big Data: Brain Signals Analysis for Therapeutic Adaptive Brain Stimulation

 

Responsive electrical neurostimulation is an emerging technology for the treatment of many brain disorders. In the Intelligent Sensory Microsystems Lab, we have been developing technologies to prevent seizures by means of responsive neurostimulation (i.e., stimulating the brain before an upcoming seizure in order to prevent the seizure from occurring). As part of this project, we have been collecting multi-terabyte datasets of clinical intracranial EEG recordings from patients being evaluated for epilepsy surgery. We are looking for talented, highly motivated individuals to help us analyze these data. Specifically, the candidate will develop machine learning algorithms for predicting the onset of seizures, detecting epileptogenic brain regions and for identifying optimum brain stimulation strategies. The following qualifications are required / preferable:

-       Solid experience in artificial intelligence and machine learning algorithms (e.g., support vector machines, RNNs, deep learning, boosting, ensemble methods)

-       Strong programming skills in MATLAB or Python

-       Experience with analyzing time series data

-       Experience with streaming data analysis

-       (Preferable) experience with electrophysiology data

-       (Preferable) experience with C and parallel programming (e.g., MPI)

 

 

5.    (NEW!) At-the-Edge Machine Learning VLSI ACCELERATORS for Energy-efficient Brain State Classification and Responsive Stimulation

 

Implementation of energy-efficient machine learning algorithms for both: (a) accurate prediction/detection of pathological brain states such as epileptic seizures; and (2) patient-tailored lifelong adaptive neurostimulation. The algorithms are currently support vector machines and will likely also include reinforcement learning / RNNs / deep learning etc in the future. These would be initially implemented on an FPGA connected in a closed loop to a human patient brain, with an ASIC implementation constraints in minds. Next these would be synthesized on a low-power implantable ASIC. This is currently a fully-digital computing architecture: on-FPGA/on-ASIC open-core microprocessor MSP430 IP combined with accelerator co-processors both for multiple feature extractors and the data classifier, as well as on-chip SRAM, etc. Please see our ISSCC 2018 for more details (in collaboration with Princeton). The project involves fully-digital computing architectures (on-chip microprocessors such as open-core MSP430 IP combined with accelerator co-processors, highly parallel accelerators, bit-level processing, asynchronous processors, etc) - first in Verilog/FPGA then fabricated in digital CMOS; novel ways of implementing both feature extraction (spatio-temporal filtering, PCA, ICA, etc) and data classification in VLSI; resources balancing between feature extraction and data classification. We are in the early phase of a 5-year clinical study of artificially-intelligent responsive brain stimulation to treat intractable epilepsy that aims to utilize this technology.

 

 

6.    (NEW!) Analog Integrated Circuits For Memristor-Accelerated Machine Learning-In-Memory (two positions)

 

6.1. This project involves mixed-signal CMOS integrated circuits that enable in-memory computing architectures such as those using RRAM (resistive RAM), with the focus on densely integrated ADC and DAC design. Topics may also include massively parallel analog computing architectures, computation in SRAM / DRAM, charge-domain signal processing, analog-to-information converters, analog-to-time converters, computational ADCs, multiplying ADCs, bit-level/bit-serial processing, reconfigurable architectures, and non-uniform sampling ADCs.

6.2. Another aspect of this project is energy-efficient computing architectures relying on memristor arrays for machine learning algorithms (e.g., support vector machines, deep learning, boosting, ensemble methods)

 

 

7.    (NEW!) Server-side/In-Cloud Machine Learning VLSI ACCELERATORS

 

This project involves digital ASIC design for implementing high-throughput processor architectures for deep learning acceleration developed in Prof. Andreas Moshovos group. We aim to implement his group’s recent advances in processor architecture in custom silicon. One example of such a processor architecture innovation can be found in:

 

J. Albericio, A. Delmás, P. Judd, S. Sharify, G. O’Leary, R. Genov, A. Moshovos , “Bit-pragmatic Deep Neural Network Computing,” 50th Annual IEEE/ACM International Symposium on Microarchitecture, Boston, Oct. 2017. (also available in online archives)

 

The project includes feasibility study, architecture-to-ASIC translation/mapping, synthesis and place and route including memory compiler, fabrication and testing). The aim is to submit a manuscript to ISSCC.

 

8.    Analog and Mixed-Signal Integrated Circuit Design for Next-Generation Brain-Chip, Skin-Chip and Other Organ-Chip Interfaces

                                                                                                                                                       

8.1.  Oversampling and Nyquist-rate analog-to-digital converters (please see our JSSC 2016 and ISSCC 2017 papers for examples of previous designs)

8.2.  Analog-to-digital converter arrays for sensory applications

8.3.  Computational analog-to-digital converters / information-to-digital converters

8.4.  Analog-to-digital converters for non-uniform / compressive data sensing

8.5.  Analog signal processing, including digitally-assisted analog design

8.6.  Analog-digital co-design / mixed-signal systems-on-chip

 

 

9.    Opto-electronic Implantable, Wearable and Disposable Integrated Circuits (CMOS Imagers) for Bio-sensing/imaging

 

9.1.  Implantable wireless CMOS contact imagers for optical monitoring of brain activity

9.2.  Optical biosensors – fluorescence, bio- and chemi-luminescence CMOS contact imagers

9.3.  CMOS optical DNA microarrays - wireless integrated circuits for on-chip fluorescent DNA analysis (including spectrum sensing photosensor arrays design, sensory information acquisition circuit design, ADC/DAC design, on-chip mixed-signal VLSI signal processing, RF transceiver circuits, high-voltage CMOS circuit design, post-CMOS on-chip microelectrode fabrication and integration with microfluidic/photonic structures).

 

 

10. Wireless Radio-Frequency Integrated Circuit Design for Implantable, Injectable, and Patch-Type Wearable Biomedical Monitors and Neurostimulators

 

10.1.                                                                                                                                                                                                                           Low-power transmitter and receiver design (e.g., UWB / pulse radio design)

10.2.                                                                                                                                                                                                                           Implantable / wearable (eg. body-area) / disposable transceivers design

10.3.                                                                                                                                                                                                                           RFIC, RFID and antenna design

10.4.                                                                                                                                                                                                                           Antenna design (including on-chip antennas and coils) and high-frequency PCB design

 

 

Most of the projects are collaborative, with participants from multiple other disciplines, mainly in Medicine, Neuroscience, Chemistry, Molecular Biology, Cell Biology, Computer Vision and Robotics. Unique opportunities exist for joint student supervision with other faculty members in Electrical and Computer Engineering (Electromagnetics – with Prof. George Eleftheriades, Photonics – with Prof. Joyce Poon), in IBBME (Neural Engineering – with Prof. Paul Yoo, Neurosurgery/Neuroscience – Prof. Taufik Valiante, Neurology/Neuroscience – Prof. Peter Carlen and others) and those in other departments (Computer Science/Vision – Prof. Kyros Kutulakos, Chemistry/Sensors – Prof. Michael Thompson, Medicine, Collaborative Program in Neurosciences, Institute of Medical Science, etc).

 

Please note that the training focus in our lab is on academic careers (future professorships), entrepreneurship (future co-founders and CTOs/CEOs of companies) and senior-level R&D positions in industry worldwide. For most of the listed positions we prefer applicants with excellent communication skills who take initiative, and who are proven leaders, problem-solvers, self-starters, and team players.

 

Qualified students interested in joining our lab are encouraged to apply for admission into our Ph.D. or M.A.Sc. degree programs as well as the post-doctoral stream. Applicants with a Bachelor degree can enroll directly into the Ph.D. program immediately or upon successful completion of the first two semesters of studies. Admitted students generally receive full financial support for the duration of their studies. The general application process is outlined at https://www.ece.utoronto.ca/graduates/admission/ You can also contact me, Prof. Roman Genov, by email at roman[AT]eecg.utoronto.ca. Please attach your CV/resume in pdf format (with GPA clearly stated) and your transcript. Sometimes I am not able to answer all email inquiries but will keep them on file until the graduate office or our team has received all of your application materials.

 

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