[robotics-worldwide] Postdoc position Autonomous Systems MBARI, California
Kanna.Rajan at mbari.org
Sun Nov 30 11:53:22 PST 2008
[apologies for multiple messages]
The Autonomous System group at the Monterey Bay Aquarium Research
Institute (MBARI) invites applications for 1 two-year postdoc position.
MBARI (http://www.mbari.org) is a private non-profit Oceanographic
research institute located in Moss Landing, California, in the heart
of the largest marine sanctuary in the United States, just outside
Silicon Valley. The institute is an inter-disciplinary research
institution guided by a strong peer relationship between scientists,
engineers and marine operations staff. MBARI pioneered the use of
Remotely Operated Vehicles (ROVs http://www.mbari.org/dmo/vessels_vehicles/tiburon/tiburon.html)
and Autonomous Underwater Vehicles (AUVshttp://www.mbari.org/auv/default.htm)
for deep ocean research and continues to be at the cutting edge of
marine robotics, marine sensor development, ocean chemistry, marine
geology, micro-biology and ecology.
The Autonomous Systems group is uniquely placed as being the only
Artificial Intelligence group within an operational oceanographic
setting anywhere. The focus of the groups effort is in automated
reasoning for embodied robust intelligence for AUVs with a focus on
Automated Planning, Execution, Constraint-Based Reasoning and Machine
Learning. The core development effort is to enable adaptive control
for AUVs to survey, sample and characterize dynamic and episodic ocean
phenomenon such as Harmful Algal Blooms, Fronts and Thin Layers which
have substantial societal impact (http://www.mbari.org/autonomy/
TREX/). Further efforts are underway to study the feasibility of goal-
based commanding for underwater feature-based SLAM, mixed-initiative
platform control and multi-vehicle coordination.
The Autonomous Systems group was established in 2005 by researchers
from NASA with extensive background in commanding spacecraft including
the twin Mars Rovers Spirit and Opportunity. A strong inter-
disciplinary effort with biological oceanographers at MBARI and others
outside has driven the design and deployment of a hybrid executive*
which enables an AUV to adaptively sample the environment using
Planning and Machine Learning techniques to inform dynamic in-situ
behavior. Publishing in peer-reviewed conferences and journals in AI,
Robotics and the Ocean Sciences and interacting with scientists in the
fields of biology, chemistry, ecology, genetics, ocean physics, marine
robotics is an important aspect of the research effort. The group
collaborates with AI and Robotics researchers in academia in the US
and Europe and hosts visiting graduate students and researchers from
within the US and Europe.
Key topic areas of interest for this position are:
1. Automated Planning and Execution
3. Machine Learning
4. Constraint-based Reasoning
5. Distributed Planning
Applicants are encouraged to communicate with the PI for project
feasibility and relevance to ongoing MBARI research. Women and
minorities are strongly encouraged to apply. Details of the
application process are at http://www.mbari.org/oed/jobs/Postdocs-2009.html
. The deadline for applications is December 11th 2008.
1. McGann, C., Py, F., Rajan, K., Ryan, J., Thomas, H., Henthorn, R.,
and McEwen, R. “Preliminary Results for Model-Based Adaptive Control
of an Autonomous Underwater Vehicle” Intnl. Symposium on Experimental
Robotics (ISER) (Athens, Greece, 2008).
2. McGann, C., Py, F., Rajan, K., Ryan, J., and Henthorn, R.
“Adaptive Control for Autonomous Underwater Vehicles” In Proc.
Assoc. for the Advancement of Artiﬁcial Intelligence, National
Conference (AAAI) (Chicago, IL, 2008).
3. McGann, C., Py, F., Rajan, K., Thomas, H., Henthorn, R., and
McEwen, R. “A Deliberative Architecture for AUV Control” In Proc.
IEEE International Conference on Robotics and Automation (ICRA)
(Pasadena, CA, May 2008).
4. McGann, C., Py, F., Rajan, K., Thomas, H., Henthorn, R., and
McEwen, R., “Automated Decision Making For a New Class of AUV
Science”, In ASLO/Ocean Sciences, Florida, 2008.
5. Py, F., Ryan, J., Rajan, K., McGann, C., Fox, M, “Adaptive Water
Sampling from an Autonomous Underwater Vehicle based on Unsupervised
Clustering”, In ASLO/Ocean Sciences, Florida, 2008
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