Job Description:
Mining and Modeling the "Antibody-ome":
While “antibody-ome” doesn't quite roll off the tongue, it captures the essence of a collaboration between the Bailey-Kellogg and Ackerman labs at Dartmouth. We’re using a combination of experimental and computational techniques to uncover patterns that relate the biophysical features of antibodies with their innate immune effector functions, and to identify mechanisms that boost development of these properties in response to vaccination or infection. We have a novel high-throughput array platform that provides unprecedented data characterizing the antibody response, and we’re collaborating closely with other labs that produce complementary data on effector function, protection, and other immune response activity, from both vaccine trials and studies of natural infection. As part of this effort, we are seeking a postdoc to help develop and apply appropriate machine learning methods. This project will provide opportunities to integrate and analyze rich sets of diverse data, to develop predictive models that will be used prospectively in vaccine development, and to optimize experiments to assess and improve the models.
The focus of this position is the development and application of computational methods. The key qualifications are a strong background in machine learning or statistical data analysis, along with experience in data integration and visualization. Familiarity with concepts of computational biology and/or immunology would be helpful, but is otherwise a benefit for you from this postdoctoral training.
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Job Information
Position Type: Post-Doctoral Research Fellowships
Reference (Job ID number):
Start Date: TBD
Duration: Full Time
Status: open
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Contact Information
Dartmouth College
Department of Computer Science
Dr. Chris Bailey-Kellogg
cbk@cs.dartmouth.edu
Tel. Number: 1-603-646-3385
http://cs.dartmouth.edu/~cbk/
How To Apply:
Please email your CV, statement of research background and interests, and contact information for 2-3 references.
Additional Info:
[Click Here to Access the Original Job Post]
Mining and Modeling the "Antibody-ome":
While “antibody-ome” doesn't quite roll off the tongue, it captures the essence of a collaboration between the Bailey-Kellogg and Ackerman labs at Dartmouth. We’re using a combination of experimental and computational techniques to uncover patterns that relate the biophysical features of antibodies with their innate immune effector functions, and to identify mechanisms that boost development of these properties in response to vaccination or infection. We have a novel high-throughput array platform that provides unprecedented data characterizing the antibody response, and we’re collaborating closely with other labs that produce complementary data on effector function, protection, and other immune response activity, from both vaccine trials and studies of natural infection. As part of this effort, we are seeking a postdoc to help develop and apply appropriate machine learning methods. This project will provide opportunities to integrate and analyze rich sets of diverse data, to develop predictive models that will be used prospectively in vaccine development, and to optimize experiments to assess and improve the models.
The focus of this position is the development and application of computational methods. The key qualifications are a strong background in machine learning or statistical data analysis, along with experience in data integration and visualization. Familiarity with concepts of computational biology and/or immunology would be helpful, but is otherwise a benefit for you from this postdoctoral training.
--------------------------------------------------------------------------------
Job Information
Position Type: Post-Doctoral Research Fellowships
Reference (Job ID number):
Start Date: TBD
Duration: Full Time
Status: open
--------------------------------------------------------------------------------
Contact Information
Dartmouth College
Department of Computer Science
Dr. Chris Bailey-Kellogg
cbk@cs.dartmouth.edu
Tel. Number: 1-603-646-3385
http://cs.dartmouth.edu/~cbk/
How To Apply:
Please email your CV, statement of research background and interests, and contact information for 2-3 references.
Requeriments :
Skills :
Areas :
Additional Info:
[Click Here to Access the Original Job Post]