http://www.biospectrumindia.com/biospecindia/features/203849/supercomputing-indian-agriculture-fast-track-mode/page/1

Project Fellow – One Post

Duration: Three Years

Fellowship : Rs.14, 000/- per month + HRA

Walk-in-interview: 16th Sept, 2013 at 10.00 am

Venue : Department of Bioinformatics, Alagappa University

Eligible candidates can attend walk in interview for the post of Project Fellow in UGC supported project entitled “Shape and chemical feature based 3D- Pharmacophore Model generation, Virtual Screening and MESP studies to identify Potential Leads for Antifungal Azoles”

Interested candidates may apply to Dr. Sanjeev Kumar Singh, Principal Investigator, Department of Bioinformatics, Karaikudi with their Curriculum Vitae along with the copies of the certificates in proof of their educational qualification and experience at skysanjeev@gmail.com

Qualification:

PG Degree in Bioinformatics / Chemistry/ Molecular Biology/ Biotechnology / Biophysics / Biochemistry / Life Sciences/ Pharmacy with minimum of 55% marks in the PG examinations

Desirable:

Research experience in Molecular modeling and CADD will be given preference. UGC-NET/ CSIR-JRF qualified candidates will get preference.

The posts are purely on temporary basis. No TA/DA will be paid for attending the interview.

Advertisement: http://www.alagappauniversity.ac.in/files/news_files/new%20advt-UGC-16sept,13.doc

Position Paper Submission Extension: October 9, 2014 
Regular Paper Authors Notification: November 3, 2014 
Position Paper Authors Notification: November 6, 2014 
Regular and Position Paper Camera Ready and Registration: November 17, 2014 

Address of the bookmark: http://www.bioinformatics.biostec.org/

More at http://www.mathivananlab.org/index.html

http://scholar.google.com/citations?user=U6PyEdYAAAAJ&hl=en

More at http://www.ircc.iitb.ac.in/IRCC-Webpage/rnd/RecruitmentGenerateCircular.jsp?srno=2013086

• the Damerau–Levenshtein distance allows insertion, deletion, substitution, and the transposition of two adjacent characters;
• the longest common subsequence (LCS) distance allows only insertion and deletion, not substitution;
• the Hamming distance allows only substitution, hence, it only applies to strings of the same length.
• the Jaro distance allows only transposition.

use Text::Levenshtein qw(distance);

 print distance("foo","four");
 # prints "2"

 my @words     = qw/ four foo bar /;
 my @distances = distance("foo",@words);

 print "@distances";
 # prints "2 0 3"

use Algorithm::LCSS qw( LCSS CSS CSS_Sorted );
    my $lcss_ary_ref = LCSS( \@SEQ1, \@SEQ2 );  # ref to array
    my $lcss_string  = LCSS( $STR1, $STR2 );    # string
    my $css_ary_ref = CSS( \@SEQ1, \@SEQ2 );    # ref to array of arrays
    my $css_str_ref = CSS( $STR1, $STR2 );      # ref to array of strings
    my $css_ary_ref = CSS_Sorted( \@SEQ1, \@SEQ2 );  # ref to array of arrays
    my $css_str_ref = CSS_Sorted( $STR1, $STR2 );    # ref to array of strings

There are many different modules on CPAN for calculating the edit distance between two strings. Here's just a selection.

Text::LevenshteinXS and Text::Levenshtein::XS are both versions of the Levenshtein algorithm that require a C compiler, but will be a lot faster than this module.

The Damerau-Levenshtein edit distance is like the Levenshtein distance, but in addition to insertion, deletion and substitution, it also considers the transposition of two adjacent characters to be a single edit. The module Text::Levenshtein::Damerau defaults to using a pure perl implementation, but if you've installed Text::Levenshtein::Damerau::XS then it will be a lot quicker.

Text::WagnerFischer is an implementation of the Wagner-Fischer edit distance, which is similar to the Levenshtein, but applies different weights to each edit type.

Text::Brew is an implementation of the Brew edit distance, which is another algorithm based on edit weights.

Text::Fuzzy provides a number of operations for partial or fuzzy matching of text based on edit distance. Text::Fuzzy::PP is a pure perl implementation of the same interface.

String::Similarity takes two strings and returns a value between 0 (meaning entirely different) and 1 (meaning identical). Apparently based on edit distance.

Text::Dice calculates Dice's coefficient for two strings. This formula was originally developed to measure the similarity of two different populations in ecological research.