Your mission
As a Simulation Engineer you are responsible for using and improving INBRAIN’s in-silico environment to simulate electromagnetic fields and their measurement with realistic biophysical sensors (neural sensing), and to simulate neurocomputational models and their modulation with stimulation. All within realistic anatomical brain models.
You will collaborate with data scientists to enable these in silico capabilities to test and rapidly iterate the development of algorithms for neural signal processing and neuromodulation. You will also collaborate with electrical and material science engineers to optimize neural sensing and neuromodulation capabilities of our neural implant. Finally, you will collaborate with a field engineer to transfer in-silico tests to in vitro bench tests to validate algorithms developed within the team and rapidly adjust and iterate on their development prior to pre-clinical in-vivo testing.
Familiarity and/or interest in Multiphysics simulations is a plus. As the project matures you will further support the development team to virtually prototype and evaluate implant designs and patient safety regarding compatibility with medical imaging (MRI, CT etc.), heat dissipation, and effect of externally applied electrosurgery and defibrillation currents.
Main responsibilities
Simulation:
- Using and improving an in-silico framework to simulate brain signals and their neuromodulation with electrical fields. This is in the scope of basal ganglia and corticothalamic networks and based on available neurocomputational and neuromodulation models.
- Integrate and maintain our current in-silico capabilities to model anatomically realistic conductivity models and the biophysical transduction of electromagnetic fields at the sensor level.
- Use the in-silico framework to test and validate signal processing pipelines and machine learning algorithms developed within the team for future pre-clinical and clinical in vivo applications.
- Manage and familiarize (if needed) with Sim4Life based simulation platforms to perform:
- Computational neuromodulation. I.e. dynamic modelling of electromagnetically induced neuronal activation, inhibition, and synchronization.
- Neural sensing. I.e. the simulation of neuro electrical signals, their conduction in biologically realistic tissues and their transduction at the sensor level.
- Computational biophysics applied to neuron tissue and membrane models.
- State-of-the-art electrode arrays for stimulation and recording from neuronal tissues.
- Work closely with Data and Algorithm engineers to design the best test environments for the desired data driven algorithmic interventions.
- Work closely with Data scientists and Neuroscientists to design and implement the simulation capabilities regarding the network dynamics and brain signals of relevance in Parkinson’s disease and affected structures.
- Build parametrized electrode arrays and expand the in-house biophysical simulation capabilities used to optimize electrode array designs and perform sensitivity analysis and evaluations.
- As the project progresses, define various electrode design concepts, optimize those for maximum activations of the neuronal targets, with a high spatial selectivity, as well as sensitive recording capabilities.
- As the project matures, receive training (if needed) in broader simulation capabilities (heat transfer, radiation and EM current induction and heat generation and dissipation) to expand the simulation capabilities and inform medical implant design and safety
Testing:
- Design bench/in-vitro test paradigms that will enable to further test and validate the algorithms previously tested and iterated using the in-silico platform.
- Work closely with field engineers to transfer in-silico paradigms to in-vitro and in-vivo setups for testing.
- Support experimental execution of relevant data collection.