EDNE Open Positions

Open Positions are posted about 6 weeks before each deadline.

Candidates should keep in mind to indicate “hiring labs” when filling in the 3-5 labs in which they are interested in the on-line application.

Please check this space in early March.

Blanke Laboratory of Cognitive Neuroscience , three positions

1) Neural Coupling Between Episodic Autobiograhical Memory & Self-Consciousness

2) Experimental Induction of Out-of-Body Experiences Using Virtual Reality & Robotics

3) Sensorimotor & Social Mechanisms of Hallucinations in Healthy Participants & Patients with Parkinson’s Disease

Courtine Laboratory of Spinal Cord Injury, two positions
1) Restoration of bladder control after spinal cord injury

2) Axon regeneration after spinal cord injury

Gerstner Laboratory of Computational Neuroscience reviews applications for each deadline in view of hiring a PhD student with a strong theory background.  Please apply directly to the EDNE program with your full application package.  No need to pre-contact the lab, since decisions are only made once all the application material has arrived through the official channel.

Ghezzi Laboratory of Neuroengineering , one position

Validation of a wide-field retinal prosthesis in large mammals – We have designed a wide-field injectable retinal prosthesis (POLYRETINA) embedding more than 10,000 photovoltaic pixels able to provide wireless stimulation to retinal ganglion cells. The preclinical validation of the prosthesis requires to perform characterization with retinal explants and tests in large animal models. The project requires the use of retinal explants to validate new design and fabrication concepts. Then, the prostheses will be placed in the eye of blind minipigs and record cortical visually evoked field potentials upon light stimulation. The candidate will join a multidisciplinary team and will work in coordination with the microengineering team developing the implant and the surgical team performing the surgical injection. The ideal candidate must have a degree in neuroscience or medicine (or related fields) and some expertise in the following activities: in-vivo electrophysiology, such as multisite extracellular recordings, ERG, and visual stimulation; experience in data processing, analysis and interpretation.

Herzog Laboratory of Psychophysics, two positions, two projects

1) Spatio-temporal processing;  Requires a good background in experimental design and math modelling/EEG recordings.

2) Healthy ageing and schizophrenia; Requires excellent statistical and programming skills.

Hill Group of Blue Brain Project, one position

Large-scale simulations of brain states in thalamocortical circuitry. This project would be to integrate data to refine biophysically detailed models of thalamocortical circuitry, run simulations on a supercomputer simulating diverse neuromodulatory conditions, including wakefulness and sleep, and evaluate changes in information integration across different conditions.

 Mathis (Alexander) Group  starting in May 2020, two positions

1) AI for Animal Behavior –  We strive to develop computer vision and machine learning tools for the analysis and quantification of animal behavior.

2) Modeling of Sensorimotor Representations – We will develop normative theories of neural systems that are trained to perform sensorimotor behaviors. Furthermore, we will compare and contrast those with data from mice performing motor skills.

Mathis (Mackenzie) Laboratory of Adaptive Motor Control, two positions

We aim to reverse engineer the neural circuits that drive adaptive motor behavior. We hope that by understanding the neural basis of adaptive motor control we can open new avenues in therapeutic research for neurological disease, help build better machine learning tools, and provide fundamental insights into brain function. Example projects include:
1) Large-scale 2-photon imaging in mice during motor behaviors. This project combines 2-photon neural recordings across the cortex with computer vision (DeepLabCut) to uncover neural circuit computations during a skilled joystick-game for mice.

2) Electrophysiology during visually and sensory guided learning. Using modern game-engines we develop new behavioral assays for learning in mice. This project pairs quantitative computer vision analysis of behavior, new behavioral paradigms, and electrophysiology.

Tools used in the lab: 2-photon imaging, electrophysiology, optogenetics, computer vision (DeepLabCut), and modeling.

Ramdya Laboratory of Neuroengineering, two positions

We investigate transgenic flies (Drosophila melanogaster) to understand how behavior is controlled and to design more intelligent robots.

Possible areas of study:

1) Data-driven neural and biomechanical modeling of limb control.  Key techniques: Computational modeling, Simulations, Confocal microscopy, Genetics

2) Electrophysiological recordings of synthetically rewired behavioral command neurons. Key techniques: Electrophysiology, Genetics, Confocal microscopy

3) Optical recordings of neuronal population dynamics for limb control. Key techniques: 2-photon microscopy, Machine learning, Genetics

Join us! There is much to discover!”

Schürmann Group of Blue Brain Project, two positions, two projects

1) Characterizing Neuromorphic Substrates

Today, the characterization of the capabilities of different neuromorphic architectures is at best empirical. Understanding in detail how their design trade-offs affect the type of models they can simulate and what enables/limits the execution performance provides an important basis for their wider adoption for research. This project will extend our performance models for neurosimulations on general purpose architectures to neuromorphic hardware. The outcome of this project has the potential to impact which type of neuromorphic circuits may get integrated into future general-purpose computing architectures.

2) Recasting Neurosimulations as a Machine Learning Problem

The recent work by other groups gave first indication that the input/output relation of a detailed neuron can be approximated by a deep neural network. Our project will research the potential of this reformulation for accelerating brain simulation. Several fundamental questions will have to be answered in this project relating to the general applicability of the reformulation to different neurons as well as synaptic configurations, the performance characteristics of this formulation in a network configuration, and the compatibility with synaptic plasticity.