Joint Ph.D. Program

The Max Planck-University of Toronto Centre (MPUTC) for Neural Science and Technology offers unique opportunities for jointly supervised PhD thesis research between the participating Max Planck Society (MPG) and University of Toronto (U of T) PIs.

How the Program Works

PhD candidates complete academic requirements at and earn PhD degrees from the U of T while being able to conduct research at a Max Planck Institute (MPI).  As a jointly supervised PhD student, you will have access to complementary facilities, equipment, and diverse intellectual feedback to increase the impact of your research. The ability to work effectively at multiple institutions, with different cultures, and in international environments is an important and highly valued skill in the global economy.  Finally, the experience will increase your job prospects as you expand your network, meet new friends and colleagues, and learn about different cultures.

Year 1 : U of T

Coursework, get started in research, pass qualifying exam.

Year 2 & 3 : MPI

Research,  visit U of T as necessary

Year 4 : MPI & U of T

Research, visit U of T as necessary
Defend thesis at U of T

Funding

  • At U of T:  PhD students receive funding according to the registered department's policy.
  • At MPI:  PhD students receive a minimum of 65% TVöD 13 work contract at the project-affiliated MPI.

How To Register

Check the available projects below for a collaborative PhD position between a participating MPG PI and the U of T PI.

Alternatively, you may contact one of the participating PIs directly to see if they can create a project:

University of Toronto PI's

Max Planck Society PI's

Mail For Inquiry

max.planck@utoronto.ca

  • Submit a graduate studies application to the department of the University of Toronto supervisor.
  • You must meet the requirements for admissions and graduate studies at the University of Toronto.
  • Send and updated CV and a 1 page single spaced proposal to the relevant supervisors and administrator in response to the job posting or for a proposed collaboration.

The MPUTC is strongly committed to diversity within its community and especially welcomes applications from racialized persons / persons of colour, women, Indigenous / Aboriginal People of North America, persons with disabilities, LGBTQ2S+ persons, and others who may contribute to the further diversification of ideas. 

Project proposal

To submit a proposal for a joint PhD project, please complete this MPUTC Joint PhD Project Proposal template.   Successful applicants will be notified on an ongoing basis.
MPUTC Joint PhD Project Proposal
MPUTC Joint Ph.D. Project  - Template to submit a proposal for a joint project

Brochure for the MPUTC Joint PhD Program

MPUTC Joint PhD Program Brochure
Max Planck Supervisor (Director, W2 Group Leader, or Scientist) Lucia Melloni 
U of T Supervisor (a faculty member appointed to the School of Graduate Studies) Kamil Uludag
Ph.D. Student TBD
Student’s U of T Department Medical Biophysics
Student’s MPI  MPI for Empirical Aesthetics
2022-2023 Enrollment

Thesis Topic : Specific Hippocampal pathways mediate episodic memory and statistical learning.

Description: The process by which new memories are layered upon prior experience and knowledge remains poorly understood. Growing evidence suggests that the hippocampus is critical for rapidly extracting regularities from the environment, a process known as statistical learning. These results, which imply generalization across episodes, are however at odd with the known role of the hippocampus in encoding individual memories. An extension of the complementary learning system accommodates this conundrum by postulating that episodic memory and statistical learning operate via different hippocampal pathways. Although this model provides a theoretical solution to a long-standing puzzle in the field, testing it in human subjects is technically challenging as it requires imaging the hippocampus with high spatial resolution to enable subfield classification and high temporal resolution to characterize the sequence of activity in the two pathways. We want to acquire unique datasets for this purpose by integrating 7T high-field fMRI with invasive electrophysiology, and electrical stimulation of specific hippocampal subfield in epilepsy patients while subjects perform associative learning and statistical learning tasks. This project seeks to establish a new multi-institution, high-throughput collaboration for large-scale, non-invasive imaging, invasive neural recording and stimulation to study memory processes in healthy subjects and patients.

Max Planck Supervisor (Director, W2 Group Leader, or Scientist) Metin Sitti
U of T Supervisor (a faculty member appointed to the School of Graduate Studies) Eric Diller
Ph.D. Student TBD
Student’s U of T Department Mechanical and Industrial Engineering
Student’s MPI Physical Intelligence
2022-2023 Enrollment

Thesis Topic : Microrobotic Electrode Placement for Neural Interfaces and Deep Brain Stimulation.

Research Theme: Develop novel tools to observe and stimulate neural activity.

Description: The project is part of a broader effort to develop micro/nanotechnology-based sensors and actuators to monitor and stimulate neural circuits in vitro and in vivo. The goal of the devices is to enable a better understanding of neurons and neural circuits, which will aid in the development of neuromedicine. The ability to map the activity of individual neurons will allow for high resolution recording of neural activity at a level not seen before. To this end, advanced probes are being developed to be implanted into brain tissue. However, the ability to place arrays of electrodes with single-neuron precision has not been achieved. In this project, the student will develop new electrode placement mechanisms based on smart material and/or magnetic actuation which allows for addressable precision placement of many electrode tips within an array. The student will work in the labs of Prof. Eric Diller at the University of Toronto and Metin Sitti at the Max Planck Institute for Intelligent Systems using multidisciplinary skills. With neurophysics collaborators, the student will develop prototypes for testing in phantom models to prove the efficacy for electrode placement and adjustment.

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