GENOV LAB OPENINGS
FOR UNDERGRADUATE and MEng STUDENTS
2023-24: List
of openings for undergraduate and MEng students
(ECE2500 MEng Projects, Research Volunteer Positions, USRA/UTEA/ESROP
Positions, Eng Sci and ECE Thesis Projects, Senior
Design Projects)
Please follow application instructions for each project posted at the
above link. In the subject line of your email message, please include keywords:
“MEng Project Application” or “Undergraduate Research Project Application”.
Please include your resume and transcript (the minimum GPA is 3.4).
FOR PhD / Direct-Entry-PhD STUDENTS AND POST-DOCTORAL FELLOWS
2024: Several
positions are available for applicants with experience in analog, mixed-signal,
RF or digital IC design (industrial experience is welcome). Applicants with
experience/interest in image sensors design, computer vision, neural
interfacing ICs and digital systems design are currently of particular interest
to us. More information on available projects is given below.
Please attach your CV/resume (WITH GPA STATED), your transcript (in pdf
format) and list of the projects below (with project number and title included)
in the order of your preference (highest first).
Starting dates are May or September of 2023-24, or on
a rolling basis any time of the year. Most of the openings listed below are
available immediately but not all may be filled. If you are
looking to apply for a PhD degree or post-doctoral studies please contact me by
email directly. The general application
process is outlined at https://www.ece.utoronto.ca/graduates/admission/
Our positions are very competitively funded.
1.
(NEW!) Transport-aware
3D Image Sensors and Cameras for Next-generation Smart Phones, Autonomous
Vehicles, Machine Vision and Artificial Vision (several positions, in
collaboration with Prof. Kyros Kutulakos
in the Computer Vision Group)
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 fields of computational
photography and computer vision,
with diverse novel applications, as unimaginable as 3D imaging outdoors, seeing against the sun, 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 (position 1 covers 1.1, 1.2, 1.3, 1.6 and position 2 covers
1.3, 1.4, 1.5, 1.6)
1.1 CMOS analog and mixed-signal
integrated circuits/systems design (e.g., pixel design, CMOS imager
circuits such as column-parallel readout amplifiers and analog-to-digital
converters (ADCs)),
1.2 Semiconductor device physics:
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);
1.3 CMOS image sensor experimental characterization using
our state-of-the art IC testing facilities both in Prof. Genov’s
and Prof. Kutulakos’ labs.
1.4 Digital systems for computer vision:
computer architecture and microprocessor IP on-chip instantiation and
programming (e.g., using open-core microprocessor IP such as TI MSP430 or ARM);
1.5 Embedded systems for computer vision:
solid knowledge of Verilog and/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 memory IP interfaces such as DDR2/3/4,
USB3 interface, Xilinx Microblaze Embedded Processor,
Xilinx SPI interface protocol, FPGA programming tools such as by Xilinx or
Altera);
1.6 Computational vision systems design and deployment
through collaborative projects with world’s top computer vision centers (such
as collaborators’ sites at Carnegie
Mellon University, Madison and Stanford).
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:
Artificially-intelligent Implantable Neural Interfaces and Neurostimulators for
Diagnostics and Treatment of Neurological Disorders (collaborations with Profs. Taufik Valiante, Jose Zariffa, Paul Yoo).
The implantation targets include both the central
nervous system (CNS) such as the brain and the peripheral nervous system such
as the vagus nerve. This thrust involves the design
of implantable and wearable CMOS analog and mixed-signal
integrated circuits/systems 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.
a. Embedded systems design for wearable brain
and PNS interfaces (using commercially available IC components)
b. Implantable wireless integrated circuits for
brain and PNS neural activity monitoring and modulation
c. Wireless brain and PNS implants with on-chip
artificial intelligence (including studying machine learning
algorithms and mapping them onto integrated circuits for on-line inference in
neural recordings – this is collaborative with our AI-oriented team members)
d. Brain
implants for optogenetic stimulation (using hybrid integration of electronics
and photonic lasers, etc)
e. 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.
E-CHEM: Miniature Electronic Chips for Nuclear
Magnetic Resonance (NMR) Spectroscopy and Electrochemical Analysis of
Biochemical Composition of Living Organisms (collaborations with Profs. Taufik Valiante, Peter Carlen,
and Mike Thompson).
This thrust involves the design of implantable and
disposable CMOS analog and mixed-signal
integrated circuits/systems for bio-chemical analysis of living organisms. The
integrated circuit design includes base-band and RF signal filtering and
amplification, ADC / DAC design, RF communication (transceivers), E&M coil
and antenna design, and in vivo experimentation with animals and human brain
slices.
3.1 Nuclear magnetic resonance (NMR) spectrometry. Nuclear magnetic resonance (NMR)
spectroscopy is a powerful analytical method for analyzing chemical compounds
by sensing local magnetic fields around atomic nuclei. It finds broad
applications in analytical chemistry and biochemistry. Conventional NMR spectrometers
are expensive and bulky, and require a large sample size. Centimeter-scale NMR
spectrometer system-on-a-chip (SoC) takes advantage of the low cost, small size
and versatile sensory and computational capabilities of modern electronic
chips.
3.2 Electrochemical sensing employs methods such as
voltammetry, amperometry and impedance spectroscopy. Integrated circuits can be implanted in the brain to
perform electrochemical sensing, imaging and modulation of neurochemicals in
the brain (please see our 2016 MDPI Sensors paper and ISSCC 2018 paper for more
details). Integrated circuits can also be implanted into
peripheral nerves for closed-loop organ-specific neuromodulation (e.g.,
electroceuticals).
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.
Machine
Learning ALGORITHMS for Big Data: Brain Signals Analysis for Therapeutic
Adaptive Brain Stimulation (collaboration with Profs. Taufik Valiante and Stark Draper)
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:
- Experience with artificial intelligence /
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
- (Preferably) experience with
electrophysiology data
- (Preferably) experience with C and parallel
programming (e.g., MPI)
5.
At-the-edge
Digital Machine Learning VLSI ACCELERATORS for Energy-efficient Brain State
Classification and Responsive Stimulation (collaboration with Prof. Taufik Valiante and Prof. Andreas
Moshovos)
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 the constraints of a digital ASIC implementation in mind. Next these would be
synthesized on a low-power implantable ASIC. We currently use a RISC-V
microprocessor combined with accelerator co-processors both for feature
extraction and inference. Please see our recent ISSCC papers for more details.
The project involves fully-digital computing architectures (on-chip
microprocessors such as RISC-V 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 (spatiotemporal
filtering, PCA, ICA, etc) and data classification in
VLSI; resources balancing between feature extraction, data classification and
wireless communication.
6.
(NEW!) Computing
in Memory: Kerneltron-2 - Next-generation Charge-Domain Mixed-Signal Machine
Learning Accelerator with Computing in DRAM (collaboration with Prof. Gert Cauwenberghs at UCSD, San Diego).
This project builds on our past success with Kerneltron
processor which set a record for energy efficiency
in computing high-dimensional linear transforms. Kerneltron
performs computing directly in memory, so energy or time are not wasted on
auxiliary memory storage, memory access and data communication. The
next-generation processor will utilize a new massively-parallel architecture,
an advance technology node (22nm or better) and a new embedded memory
technology (DRAM, NVRAM), all well suited for accelerating deep neural networks
(DNNs), convolutional neural networks (CNNs) and other network types at the
edge. Visits to San Diego are possible/likely.
The following qualifications are preferred:
-
Experience in the mixed-signal circuit design (such
as ADC design)
-
Understanding of memory circuits (such as embedded
NVRAM / DRAM / SRAM, sense amplifiers, SERDES, high-speed I/O etc)
-
Interest in working in advanced CMOS nodes (such as
22nm CMOS or SOI process)
-
Knowledge of machine learning algorithms (DNN, CNN,
backpropagation, etc)
-
Availability to travel to San Diego
7.
At-the-edge
VLSI ACCELERATORS Machine Learning near the sensor (e.g., within an implant, within
an image sensor, etc; collaboration with Prof.
Andreas Moshovos)
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.
The project includes feasibility study,
architecture-to-ASIC translation/mapping, synthesis and place and route
including memory compiler, IC fabrication and testing. The aim is to submit a
manuscript to ISSCC.
8.
Implantable Electrophysiology: Deployment
of State-of-the-Art Intracranial EEG Recording and Neruostimulation
Systems in Rodent Models of Neurological Disorders (in collaboration with Profs. Taufik Valiante, Paul Yoo and Jose Zariffa at BME / School or Medicine / Toronto Western
Hospital)
We are looking for a graduate student who is
interested in a project involving custom design and neurosurgical implantation of novel
intracranial EEG microelectrodes (from Genov) into animal
models of disorders of the central (Valiante) and
peripheral (Yoo, Zariffa)
nervous system. The student may also perform signal processing of the EEG
signals to study the signal characteristics of the EEG from an animal model of
a neurological disorder and the effects of treatment. The student would be
expected to become skilled and independent for
electrophysiological experimentation, and to interface between several collaborating
labs.
OTHER
ONGOING PROJECTS WITH PERIODIC VACANCIES:
9.
Analog and Mixed-Signal
Integrated Circuit Design for Next-Generation Brain-Chip, Skin-Chip and Other
Organ-Chip Interfaces
a. Oversampling and Nyquist-rate
analog-to-digital converters (please see our recent ISSCC papers for examples
of previous designs)
b. Analog-to-digital converter arrays for sensory
applications
c. Computational analog-to-digital converters /
information-to-digital converters
d. Analog-to-digital converters for non-uniform /
compressive data sensing
e. Analog
signal processing, including digitally-assisted analog design
f. Analog-digital co-design / mixed-signal
systems-on-chip
10. Opto-electronic
Implantable, Wearable and Disposable Integrated Circuits (CMOS Imagers) for
Bio-sensing/Neuro-imaging
a. Implantable wireless CMOS contact imagers for
optical monitoring of the brain activity
b. Coded-exposure and coded-aperture imaging of
the brain neural activity
c. Optical biosensors – fluorescence,
bio-luminescence CMOS contact imagers
11. Wireless RF Integrated Circuit Design for
Implantable, Injectable, and Wearable-patch Health Monitors and
Neurostimulators
a. Low-power transmitter and receiver design
(e.g., UWB / pulse radio design)
b. Implantable / wearable (eg.
body-area) / disposable transceivers design
c. RFIC,
RFID and antenna design
d. Antenna
design (including on-chip antennas and coils) and high-frequency PCB design
Most positions are in ECE,
ECE-BME Collaborative Program, or BME. If you have
received/applied for a NSERC or OGS graduate scholarship or have another
funding source, please inform me of this.
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 BME (Neural Engineering – with Prof.
Paul Yoo, Neurosurgery/Neuroscience – Prof. Taufik Valiante, and others) and
those in other departments (Computer Science/Vision – Profs. Kyros Kutulakos and David
Lindell, Chemistry/Sensors – Prof. Andre Simpson, 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. Often,
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.
Advantages
of the City of Toronto and the University of Toronto
