2 postdoc,Computational Biology, NYC

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Two postdoc positions are currently available-Computational Biology, New 
York City

General guideline

Prospective candidates should have a recent PhD degree in computer science, 
mathematics, bioinformatics/computational biology discipline and high 
motivation to pursue independent research in computational biology. 
Applicants are expected to have a solid background in programming and 
computational techniques, with a working knowledge of molecular biology and 
genetics being highly desirable.  

Specific guideline

Position 1: Applicants who desired to focus on method developments and 
software development:

Prospective candidates should have a recent PhD degree in computer science 
specialized in machine learning, mathematics, statistics or physics. Strong 
working experiences in Bayesian networks and other graphical models is 
highly preferred.  Candidate must have strong programming skills in C/C++/
Java, Matlab and R. Programming skills in other language is a plus. Basic 
knowledge in biology and hands-on experience in computational biology is 
highly desired but not required. The candidate will be responsible for 
developing cutting-edge machine learning approaches based on graphical 
models and other mathematical models, and is expected to develop software 
platforms towards real-world human disease network modeling and drug target 
prediction by working closely with disease modeling team.

Position 2: Applicants who desired to focus on real-world disease modeling:

Prospective candidates should have a recent PhD degree in computer science, 
bioinformatics (computational biology) or biology science. Candidate must 
have strong knowledge in biology, genomics, and hands-on experience in 
computational biology projects involves analyzing and integrating omics data
. Candidate should have a good programming skills in C/Java, Matlab or R. 
Programming skills in other language is a plus. Basic knowledge about 
graphical models, machine learning approaches is required. Strong 
understanding on Bayesian network is highly desired, but not required. The 
candidate will be responsible for integrating and analyzing multi-scale 
omics data and leverage cutting-edge method to reconstruct disease network 
and drug targets validation by working closely with method development team 
and laboratory collaborators.

Note:

Exceptional candidate have both strong machine learning background and 
biology knowledge can be considered to work cross projects and fields.

Contact:

Please send CV and three reference letters to [e-mail]