[Comp-neuro] IARPA MICrONS Technical Supervisor Position (Washington, DC)
Alyssa Picchini Schaffer
apschaffer at simonsfoundation.org
Wed Mar 15 18:59:24 CET 2017
Please contact David Markowitz if interested: david.markowitz at iarpa.gov
*Machine Intelligence from Cortical Networks (MICrONS) Technical Supervisor*
Despite significant progress in machine learning over the past few years,
today’s state of the art algorithms are brittle and do not generalize well.
In contrast, the brain is able to robustly separate and categorize signals
in the presence of significant noise and non-linear transformations, and
can extrapolate from single examples to entire classes of stimuli. This
performance gap between software and wetware persists despite some
correspondence between the architecture of the leading machine learning
algorithms and their biological counterparts in the brain, presumably
because the two still differ significantly in the details of operation.
The MICrONS program aims to achieve a quantum leap in machine learning by
creating novel machine learning algorithms that use neurally-inspired
architectures and mathematical abstractions of the representations,
transformations, and learning rules employed by the brain. To guide the
construction of these algorithms, performers will conduct targeted
neuroscience experiments that interrogate the operation of mesoscale
cortical computing circuits, taking advantage of emerging tools for
high-resolution structural and functional brain mapping. The program is
designed to facilitate iterative refinement of algorithms based on a
combination of practical, theoretical, and experimental outcomes:
performers will use their experiences with the algorithms’ design and
performance to reveal gaps in their understanding of cortical computation,
and will collect specific neuroscience data to inform new algorithmic
implementations that address these limitations. Ultimately, as performers
incorporate these insights into successive versions of the machine learning
algorithms, they will devise solutions that can achieve human-like
performance on complex information processing tasks with human-like
Requirement 1: PhD in neuroscience, physics or a related discipline with 4
years of experience studying coding and computation in high-throughput
recordings of neural activity from behaving animals using quantitative
methods, such as dimensionality reduction, time series analysis, and
machine learning approaches.
Requirement 2: Demonstrated experience in at least 3 of the following
disciplines: high-throughput electrophysiology, multi-photon microscopy,
optogenetics, circuit mapping, methods for manipulating cognitive processes
in behaving animals.
Requirement 3: Training and demonstrated capabilities in applied
mathematics and computer science. Must be proficient with at least one
numerical analysis and statistics framework (e.g. NumPy, Matlab, R) and one
general-purpose programming language (e.g. Python, Java, C).
Requirement 4: Two years of experience coordinating collaborative projects
with diverse technical contributors and aggressive timelines for achieving
results, as evidenced by research publications, public data sets or other
artifacts of work.
Desired 1: Strong background in systems neuroscience, with at least 2 years
of experience studying the circuit basis of cognition.
Desired 2: Strong background in machine learning (ML), with at least 2
years of experience using modern ML techniques to support scientific
research or solve other challenging problems.
Desired 3: Formal training in theoretical neuroscience.
Desired 4: Professional software development experience.
Desired 5: Experience using cloud computing technologies.
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