postdoctoral and doctoral fellowships

[Tandy Warnow]

             Doctoral and Postdoctoral Fellowships 
                 in Computational Biology


The Computational Biology Training Program at the University
of Pennsylvania has several fellowships available for highly
qualified students and postdoctoral researchers.  No previous
experience with computational biology is required!  The
program enjoys the participation of many faculty throughout
the University, drawing from the schools  of Medicine,
Engineering, and Arts and Sciences, and has participants
from nearby Biotech companies as well.

For the doctoral fellowships, applicants should be interested
in pursuing a PhD in Computer Science, Biology, Genetics, Mathematics,
or Statistics.  Cross-disciplinary training is provided.

For postdoctoral fellowships, applicants should already have
a PhD in one of the relevant fields (see above), and should have
interest and ability in both scientific and quantitative 
reasoning.   Research areas of interest to the program
are primarily: multiple sequence alignment, sequence comparison,
molecular evolution and phylogenetics, bioinformatics, biological
databases, physical and genetic mapping, and statistical inference in Biology.

Additional information is given below.

**************************************************************
APPLICANTS FOR THE DOCTORAL PROGRAM: 
If you are applying for the doctoral programs, you will have to
apply directly to one of the participating departments.  We do not
offer an interdisciplinary PhD, but rather supplementary training.
Information on how to apply to a doctoral program is given below.
After you have applied to the appropriate doctoral program you should
contact us by email, letting us know which department you have
applied to.  We will keep track of your application, and if it
is accepted, we will consider you for funding through our training
program.

APPLICANTS FOR POSTDOCTORAL POSITIONS:
If you are applying for a postdoctoral position, please send us
the following: 
           (a)  Completed application form.
           (b)  Statement (maximum one page) of your research 
                interests, academic goals,  and why you are 
                interested in computational biology.
           (c)  Four letters of reference -- these should be 
                     sent directly by the referees.
           (d)  Curriculum vita.


                    Yours sincerely,


                       Dr. Warren Ewens (PI), 
                       Computational Biology Training Program
                       Department of Biology
                       University of Pennsylvania
                       Philadelphia PA 19104
                       




    University of Pennsylvania Training Program in
            Computational Biology



The University of Pennsylvania has established an 
interdisciplinary training program for PhD students 
and postdoctoral researchers in computational biology. 
Areas of  interest include:
     biological databases, 
     multiple sequence alignment, 
     molecular evolution and phylogeny construction, 
     physical and genetic mapping, 
     sequence search and analysis, 
     statistical methods, 
     discrete algorithms and combinatorial 
         optimization in biology.

The research training program provides core training
in molecular biology and genetics, discrete algorithms,
mathematical modelling, and probability and statistics,
so that important biological problems can be addressed
effectively through a collaborative effort between 
researchers in these different fields. 
Advanced training draws on the expertise of the faculty, and
includes both advanced seminars and laboratory research
opportunities.

Participating faculty include the following:



Peter Buneman (Computer and Information Science):
Programming languages: applicative 
and functional languages, type systems.

Fan Chung (Mathematics):
Combinatorics and algorithms.

Susan Davidson (Computer and Information Science):
Real-time database systems: language 
support and formal methods for distributed real-time programs.

Arthur Dunham (Biology): Mathematical models at the interface 
of physiological ecology and population dynamics.

Joe Ecker (Biology):
Biochemical mechanisms involved in plant hormone signalling;
genome mapping.


Warren Ewens (Biology):
Mathematical population genetics.

Ellis Golub (Biochemistry, Dental School):
The relationship between protein sequence, structure and 
function.

Greg Guild (Biology):
Sequential activation of ecdysone-regulated genes in 
Drosophila development; mechanisms of 
transcriptional regulation.

Aravind Joshi (Computer and Information Science):
Problems that overlap computer science and linguistics.

Sampath Kannan (Computer and Information Science):
algorithms in computational biology, complexity theory,
randomization and computation.

Haig Kazazian (Genetics):
The analysis of mutational mechanisms in humans.

Steve Liebhaber (Genetics):
DNA structure-function relationships.

Mitch Marcus (Computer and Information Science):
Natural language processing.

Max Mintz (Computer and Information Science):
Decision making under uncertainty.

Chris Overton (Genetics):
Genome informatics and biological databases.

Peter Petraitis (Biology):
Community ecology of marine ecosystems:
theoretical ecology.

David Roos (Biology):
Molecular genetics and cell biology of protozoan parasites; Host-pathogen 
interactions; Eukaryotic evolution.

David Searls (Genetics):
Linguistics of biological sequences; genome informatics.

Neil Shubin (Biology):
Evolution of developmental patterns; origins of the vertebrate limb; 
Comparative molecular and paleontological phylogenies.

Eero Simoncelli (Computer and Information Science):
Representation and analysis of visual imagery: 
distributed parallel representation and computation.

Rich Spielman (Genetics):
Genetics of susceptibility to complex human diseases.

Chris Stoeckert (Children's Hospital of Philadelphia):
Understanding the regulation of fetal and adult globin genes in human 
adults.

Santosh Venkatesh (Electrical Engineering):
computational learning theory.

Tandy Warnow (Computer and Information Science):
combinatorial and graph-theoretic algorithms for 
evolutionary tree construction.

All applicants (predoctoral and postdoctoral) to this interdisciplinary 
program should contact:



           Computational Biology Training Program
           Department of Biology
           University of Pennsylvania
           Philadelphia, PA 19104-6018
           compbio at central.cis.upenn.edu

Admission to the predoctoral program requires acceptance
into a regular PhD program at the University 
of Pennsylvania. For application materials
contact one of the following:



Graduate Admissions
Department of Computer and Information Science
200 S. 33rd Street
University of Pennsylvania, Philadelphia PA 19104-6389


Graduate Admissions 
Department of Biology
University of Pennsylvania
Philadelphia, PA 19104-6018



Biomedical Graduate Studies (Molecular Biology)
240 John Morgan
University of Pennsylvania
Philadelphia, PA 19104-6064






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Please return this form with the information filled out to 
          Computational Biology Training Program
          Department of Biology
          University of Pennsylvania
          Philadelphia PA 19104-6018
          compbio at central.cis.upenn.edu



                  Computational Biology at Penn
                          Application

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Address

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Degree(s)

Coursework (undergraduate and graduate) in Mathematics, Computer Science,
        Probability and Statistics:

Coursework (undergraduate and graduate) in Biology, Molecular Genetics, etc.:


Coursework (undergraduate and graduate) in other relevant disciplines:

Undergraduate GPA and institution

Graduate GPA and institution

Research interests