(Additional openings for MEng Project Students and Undergraduate Students are here (ads are in the pdf files))


Starting dates are Sept., May or Jan. 2017-2018, 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.


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.



Competitively-funded and volunteer openings include the following research positions:


1.    (NEW!) COMPUTATIONAL CAMERAS: Transport-Aware CMOS Image Sensors and Cameras Design: Seeing around the Corner and “Beyond” (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!

     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:

1.1. analog and digital (mixed-signal) VLSI circuits design (e.g., CMOS imager circuits such as column-parallel amplifiers and ADCs),

1.2. 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. 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),

1.4. 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.    E-PHYS: Implantable and Wearable Brain-Chip Interfaces/Neurostimulators for Diagnostics and Treatment of Neurological Disorders (these positions can be cross-appointed through ECE-IBBME Collaborative program, of IBBME program)


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

2.2.  CMOS implantable wireless integrated circuits for brain neural activity monitoring and modulation (including RF transceiver design, inductive powering circuit design, ADC/DAC design, on-chip mixed-signal VLSI signal processing, integration with MEMS microelectrodes, and in vivo experimentation with animals)

2.3.  CMOS 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)

2.4.  CMOS brain implants that image, monitor and modulate brain neurochemistry (including sensory information acquisition circuit design, circuits and systems for cyclic voltammetry, amperometry and impedance spectroscopy, ADC/DAC design, on-chip mixed-signal VLSI signal processing, RF transceiver circuits, post-CMOS on-chip microelectrode fabrication and integration with microfluidic structures);

2.5.  CMOS optogenetic brain implants

2.6.  Selected topics in neuroscience, neurology, neurosurgery, electrophysiology, animal neurosurgery, epileptology (experience or interest in electrical engineering and integrated circuit design is of benefit)



3.    E-CHEM:  Fully-Wireless Single-Chip Wearable / Implantable / Disposable Microsystems for Electrochemical Diagnostics and Therapy, both IN VIVO and IN VITRO (these positions can be cross-appointed through ECE-IBBME Collaborative program, of IBBME program)


3.1.  (NEW!) CMOS circuits and systems for electrochemical sensing and imaging of neurochemicals in the brain (please see our paper 2016 MDPI Sensors for more details).

3.2.  (NEW!) CMOS peripheral nerve interfaces and electroceuticals (e.g., responsive micro-stimulators)

3.3.  CMOS electrochemcial DNA microarrays - wireless integrated circuits for on-chip amperometric DNA analysis (including sensory information acquisition circuit design, circuits and systems for cyclic voltammetry, amperometry and impedance spectroscopy, ADC/DAC design, on-chip mixed-signal VLSI signal processing, RF transceiver circuits, post-CMOS on-chip microelectrode fabrication and integration with microfluidic structures).

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



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


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

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

4.3.  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).



5.    (NEW!) ALGORITHMS FOR BIG DATA: Machine Learning for Brain Data Mining and Therapeutic 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, 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)



6.    (NEW!) VLSI ACCELERATORS FOR BIG DATA: Energy-efficient Digital (Verilog + Synthesis) or Mixed-signal IC (Computation-in-memory) Implementation of State-of-the-Art Machine Learning Algorithms


6.1.  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). This is done first in Verilog/FPGA then fabricated in digital CMOS.

6.2.  Mixed-signal CMOS computing architectures (massively parallel architectures, computation-in-memory, computation in RRAM / MEMRISTOR-arrays / 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, non-uniform sampling ADCs, asynchronous processors).

6.3.  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.

6.4.  Energy-efficient hardware implementation of machine learning algorithms (e.g., support vector machines, deep learning, boosting, ensemble methods)



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


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

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

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

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

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

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



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


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

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

8.3.  RFIC, RFID and antenna design

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



9.    Technical Manuscript Writing (volunteer and/or internal positions to complete/co-author partially completed manuscripts, and carry them through submission and revision stages)


9.1.  A journal review paper on information-to-digital converters. This manuscript will survey a number of existing multiplying-ADC (MADC) circuits previously developed in our lab for applications to information-to digital conversion. All circuits have been fabricated and tested, with resulting data available – a unifying framework needs to be presented with all existing data included. Qualifications required:

-       Outstanding technical writing skills

-       Good knowledge of the theory of ADCs (SAR ADC, algorithmic ADC, dual-slope ADC, first-order oversampling ADC, etc)

-       (Preferable) understanding of analog signal processing circuits (switched-capacitor circuits, discrete-time signal processing)

-       (Preferable) understanding of basic linear algebra (matrix operations, linear transforms), signal processing theory (sampling theorem, FIR filtering, compressive sensing, etc)

-       (Beneficial) experience in ADC design

9.2.  A short journal paper on microelectrodes for in vitro electrophysiology. We have previously fabricated a novel penetrating microelectrode for in vitro electrophysiological studies of brain slices. Brain signals recordings from such slices have been performed. Additional analysis of properties of such electrodes needs to be possibly performed, including numerical simulation of neural stimulation properties of these electrodes (e.g., using biological neural network simulation software such as Neuron or Brain). Qualifications required:

-       Outstanding technical writing skills

-       Understanding of biological neural networks and electrophysiology

-       Ability to quickly learn how to simulate the spatial effects of injected electric charge onto neurons and networks of neurons (degree of excitation, inhibition, etc)

9.3.  A journal paper on CMOS multiplying dual-slope ADC circuits reusability for electrochemical sensing and temperature regulation in applications of on-CMOS molecular sensing. Qualifications required:

-       Outstanding technical writing skills

-       Understanding of electrochemical sensory methods (affinity-based electro-chemical sensing, amperometry, cyclic voltammetry, impedance spectroscopy)

-       Basic knowledge of molecular biology (DNA amplification, PCR, etc)

-       Understanding of analog integrated circuits and basic control theory (dual-slope ADC, band-gap voltage references, PID controllers, frequency response analysis, etc)



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 You can also contact me, Prof. Roman Genov, by email at roman[AT] 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.