Scientist I Biophysical Systems and Signals

Department: 2290-MAT
Location: Seattle

Our mission at the Allen Institute for Brain Science is to accelerate the understanding of how the human brain works in health and disease. By implementing a team science approach on a large scale, we strive to generate useful public resources, drive technological innovations and discover fundamental brain properties through integration of experiments, modeling and theory.

POSITION SUMMARY

We are seeking to fill a position at the level of Scientist I to work on the relationship between neural systems, associated signals emerging from neural functioning (such as extracellular recordings, etc.) and reduced representations of such functioning.

More specifically, the SciI will (i) design and conduct simulations with an existing large-scale computational model of cortex consisting of a multitude of reconstructed, interconnected and biophysically realistic neurons emulating signals as typically measured in vivo (e.g. [Schomburg, Anastassiou et al, J Neurosci, 2012; Reimann, Anastassiou et al, Neuron, 2013; Taxidis, Anastassiou et al, Neuron, 2015); (ii) analyze simulations to define an effective mapping between neural functioning and measures of neural activity; (iii) compare computational findings with in vivo e-phys recordings (e.g. http://observatory.brain-map.org/visualcoding)

ESSENTIAL DUTIES AND RESPONSIBILITIES

  • Computational modeling of neurons and networks. 
  • Data integration, analyses and visualization.
  • Integration with ongoing experimental efforts.
  • Preparation of regular written and oral reports.
  • Maintain clear and accurate communication with supervisor, team members and external collaborators.
  •  Publish/present findings in peer-reviewed journals/scientific conferences.

QUALIFICATIONS

  • PhD degree in computational neuroscience, physics, applied mathematics, bioengineering, applied mathematics or related field.
  • Strong background in scientific computing; experience in computational neuroscience is preferred, but other strong applicants will be considered (with background e.g. in computational physics, applied mathematics, biophysics, machine learning and related disciplines). Experience with parallel computing is a plus as well as familiarity with high-level programming languages such as Python.
  •  Ability to meet aggressive timelines and deliverables in a collaborative environment.
  •  Strong publication record.
  •  Experience in pursuing research projects in collaborative fashion.
  •  Proven independent thinking and flexibility.
  •  Familiarity with in vitro and in vivo electrophysiological monitoring techniques and data analyses.
  •  Strong written and verbal communication skills.



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