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

 

(MENG Projects and Undergraduate Summer / Thesis Projects openings in these areas are also available - plus additional specific 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 may 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 STATED), your transcript (in pdf format) and list of the projects below (with project number included) in the order of your highest preference. 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 (two positions, collaboration with Prof. Kyros Kutulakos in the Computer Vision Group as well as with Carnegie Mellon University Robotics Institute and Stanford Computational Imaging Lab)

 

     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   3D cameras deployment at world’s top computer vision centers (such as collaborators cites at Stanford, Carnegie Mellon University and Rice University).

 

     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 (collaborations with Profs. Taufik Valiante, Peter Carlen, Paul Yoo).

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 (collaborations with Profs. Taufik Valiante, Peter Carlen, Mike Thompson, Paul Yoo).

                                                                                                                                                                        

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 2018 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 (collaboration with Profs. Taufik Valiante, Stark Draper, and Jeremie Lefebvre)

 

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 (collaboration with Prof. Taufik Valiante and Prof. Naveen Verma at Princeton)

 

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 a digital 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 Bio-Inspired Memristor-Accelerated Machine Learning-In-Memory for Medical/Implantable Applications (two positions, collaboration with Prof. Dominique Drouin at Laval University)

 

The modern world continues to become more interconnected with more and more sensors coming online. Novel computing systems are much needed to handle “big” heterogeneous and unstructured data processing. Since conventional systems are fundamentally not adapted to this class of problems, alternative solutions are envisioned and demand a complete shift in the computing paradigm, architecture and technology. To this end, the bio-inspired computing models are a promising solution to conventional computing since these systems can manage multi-sensory inputs with a very large bandwidth in real time and with low energy consumption. Along these lines, artificial neural networks have experienced renewed interest in recent years, outperforming humans in visual recognition tasks and gaming. Nevertheless, such systems would benefit immensely from a dense, parallel and distributed memory (synapses) along the computing nodes (neurons). In this project we will build an efficient and versatile system for the future development of machine learning hardware. We will implement a scalable, flexible and innovative strategy for the implementation of the synaptic weight and the associated multiply and accumulate (MAC) operation, one of the most demanding operations for efficient ML hardware. We will achieve on an optimum balance between CMOS flexibility /performance and the density/energy efficiency of computation in emerging memory devices (resistive memory synapses). This will take place at two levels of integration: (i) we will use an advanced system-in-package approach in order to optimize heterogeneous integration of memory devices on CMOS chips. Here, we will design and fabricate memory chips interconnected via flip-chip technology on an active interposer which will ensure dynamic signal management and routing while minimizing memory device variability and preserving CMOS design flexibility. (ii) We will implement a massively parallel and dense memory array via multiple passive crossbar interconnection in a system-on-chip strategy. Active amplification between passive crossbars will enable ultra-high memory density while preserving optimal control of the memory devices.

 

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 (the ADCs and DACs will control read and write operations for the RRAM). 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)

 

Frequent travel to (the beautiful and one of the oldest cities in North America) Quebec City will be expected.

 

 

7.    (NEW!) Server-side/In-Cloud Machine Learning VLSI ACCELERATORS (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. (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.    (NEW!)  Wireless Electrophysiology: Deployment of a State-of-the-Art Intracranial EEG Recording System in a Rodent Model of Epilepsy (primary supervision by Dr. Peter Carlen at Toronto Western Hospital)

 

Drs. Peter Carlen (IBBME, Physiology and Medicine) and Roman Genov (ECE and IBBME) are looking for a graduate student who is interested in a project involving implantation of specialized intracranial EEG electrodes (from Genov) into animal models of epilepsy (Carlen). This student will then perform signal processing of the EEG signals (guidance from Dr. Berj Bardakjian) to study the signal characteristics of the EEG from an animal model of epilepsy and the effects of treatment. In addition to the usual cerebral implantations of EEG electrodes, we are also recording from the brainstem in rats, a novel approach with great biological significance for understanding epilepsy. The student would be expected to become skilled and independent for electrophysiological experimentation, to interface with the Genov team implementing state-of-the-art EEG electrode and signal acquisition technology, and to analyze the data using advanced signal processing techniques under the guidance of Dr. Bardakjian. The student could start asap, even if the official enrolment for a degree is a few months away.

 

 

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

                                                                                                                                                       

9.1  Oversampling and Nyquist-rate analog-to-digital converters (please see our JSSC 2016 and ISSCC 2017, 2018 papers for examples of previous designs)

9.2  Analog-to-digital converter arrays for sensory applications

9.3  Computational analog-to-digital converters / information-to-digital converters

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

9.5  Analog signal processing, including digitally-assisted analog design

9.6  Analog-digital co-design / mixed-signal systems-on-chip

 

 

10. Opto-electronic Implantable, Wearable and Disposable Integrated Circuits (CMOS Imagers) for Optogenetic Photonic Stimulation and Bio-sensing/Neuro-imaging

 

10.1  Implantable wireless CMOS contact imagers for optical monitoring of brain activity

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

10.3  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).

 

 

11.  Wireless RF Integrated Circuit Design for Implantable, Injectable, and Wearable-patch Health Monitors and Neurostimulators

 

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

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

11.3  RFIC, RFID and antenna design

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

 

 

 

 

Most positions are in ECE, ECE-IBBME Collaborative Program, or IBBME. 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 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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