Here’s a curated list of the top websites offering bioinformatics job opportunities and postdoctoral fellowships worldwide.

1. General Bioinformatics Job Portals

These platforms are ideal for bioinformaticians seeking jobs in diverse sectors:

• Nature Careers: A trusted resource for job seekers in the sciences, Nature Careers offers bioinformatics roles globally. Their specialized search function allows you to filter jobs by keyword, location, and more.

  • Explore Bioinformatics Jobs on Nature Careers

• Science Careers: A job board from the AAAS, this site focuses on STEM jobs, including numerous bioinformatics opportunities in academia and industry.

• Euraxess: Euraxess is the go-to platform for researchers looking for jobs, fellowships, and funding across Europe and beyond. It lists both bioinformatics roles and research grants.

  • Search Bioinformatics Jobs on Euraxess

• ResearchGate Jobs: ResearchGate is widely known as a platform for researchers to share publications, but it also has a robust job board featuring bioinformatics positions globally.

• FindAPostDoc: This site is dedicated to helping postdoctoral researchers find positions, with bioinformatics being a popular category.

• Academic Positions: Targeting academic roles worldwide, Academic Positions lists bioinformatics jobs at universities and research institutions.

• PostdocJobs.com: Specializing in postdoctoral roles, this platform is a great resource for early-career researchers looking for bioinformatics-related positions.

• Scholarship Positions: In addition to jobs, Scholarship Positions provides information on scholarships, fellowships, and grants related to bioinformatics.

2. Fellowship and Training Opportunities in Bioinformatics

For those seeking fellowships or specialized training, these organizations offer postdoctoral programs, grants, and research opportunities:

• NIH Office of Intramural Training and Education: The National Institutes of Health offer extensive research training programs for postdocs, including those in bioinformatics.

• NSF Research Opportunity Awards: The National Science Foundation funds bioinformatics research at predominantly undergraduate institutions, providing fellowships and grants.

• Top U.S. Universities: Many prestigious U.S. institutions, including Harvard, Berkeley, Yale, MIT, Johns Hopkins, UCSD, and Cornell, offer postdoctoral opportunities in bioinformatics.

3. Country-Specific Job and Fellowship Resources

If you're targeting a specific region, these platforms offer bioinformatics opportunities tailored to their respective countries:

Canada

• CAPS/ACPP: The Canadian Association of Postdoctoral Scholars provides a job board, including bioinformatics roles in academia.
• Banting Postdoctoral Fellowships: A prestigious fellowship program for postdocs in bioinformatics and related fields.
• Mitacs Elevate: A Canadian initiative offering fellowships to connect postdoctoral researchers with industry partners.

United Kingdom

• UKRI: The UK Research and Innovation body funds bioinformatics research and offers various grants.
• The Royal Society: Provides funding schemes for researchers in bioinformatics.
• Marie Skłodowska-Curie Actions: The MSCA funds fellowships and doctoral programs across Europe, including bioinformatics-related projects.
• Wellcome Trust: Offers research funding and career development opportunities in health-related fields, including bioinformatics.

Europe

• EMBO Fellowships: The European Molecular Biology Organization supports bioinformaticians through fellowships and career grants.
• Max Planck Society: A leading research organization offering bioinformatics positions and fellowships across Europe.
• Helmholtz Association: A major research organization in Germany offering bioinformatics roles in various disciplines.
• Leibniz Association: Offers research opportunities, including bioinformatics, across its numerous institutes.

Australia and New Zealand

• Australian Research Council: Offers funding and research schemes, including in bioinformatics.
• Top Universities: Universities like Sydney, Melbourne, and Queensland have research programs in bioinformatics.

Asia

• Japan Society for the Promotion of Science (JSPS): Offers fellowships for international researchers in bioinformatics.
• Top Institutions: Universities like NUS, CAS, and IISc are leading hubs for bioinformatics research.

Middle East

• Qatar Research, Development, and Innovation (QRDI): Offers research opportunities in bioinformatics.
• KAUST: A leading university in Saudi Arabia offering bioinformatics research positions.

Africa

• African Academy of Sciences: Provides career opportunities and research funding in bioinformatics across Africa.

Conclusion

The field of bioinformatics is full of exciting opportunities for those with the right skills. Whether you are looking for a postdoc position, research funding, or a long-term job in industry, these platforms are an excellent starting point. Explore, apply, and take the next step in your bioinformatics career!

Faculty facilitator/Founder Director for two start-up companies (Leadinvent incubated at IIT, Delhi from 2006-2009 & Novoinformatics, under incubation at IIT Delhi since 2011).

Research Interest
Genome Analysis, Protein Structure Prediction and Drug Design.

Link @ http://www.scfbio-iitd.res.in/

We use algorithms every day. For example, a recipe for baking a cake is an algorithm. Most programs, with the exception of some artificial intelligence applications, consist of algorithms. Inventing elegant algorithms -- algorithms that are simple and require the fewest steps possible -- is one of the principal challenges in programming. An algorithm is a description of a procedure which terminates with a result. In other words an algorithm is a set of instructions, sometimes called a procedure or a function, that is used to perform a certain task. This can be a simple process, such as adding two numbers together, or a complex function, such as adding effects to an image. For example, in order to sharpen a digital photo, the algorithm would need to process each pixel in the image and determine which ones to change and how much to change them in order to make the image look sharper.

In mathematics, computer science, and related subjects, an algorithm is an effective method for solving a problem using a finite sequence of instructions. Algorithms are used for calculation, data processing, and many other fields.
Each algorithm is a list of well-defined instructions for completing a task. Starting from an initial state, the instructions describe a computation that proceeds through a well-defined series of successive states, eventually terminating in a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate randomness.

History

The origin of the term comes from the ancients. The concept becomes more precise with the use of variables in mathematics. Algorithm in the sense of what is now used by computers appeared as soon as first mechanical engines were invented.
The word algorithm comes from the name of the 9th century Persian Muslim mathematician Abu Abdullah Muhammad ibn Musa Al-Khwarizmi. The word algorism originally referred only to the rules of performing arithmetic using Hindu-Arabic numerals but evolved via European Latin translation of Al-Khwarizmi's name into algorithm by the 18th century. The use of the word evolved to include all definite procedures for solving problems or performing tasks.
The algorithm of Archimedes gives an approximation of the Pi number.
Eratosthenes has defined an algorithim for retrieving prime numbers.
Averroès (1126-1198) was using algorithmic methods for calculations.
Adelard de Bath (12 th) introduces the algorismus term, from Al-Khwarizmi.
During the 1800's up to the mid-1900's:

- George Boole (1847) has invented the binary algebra, the basis of computers. Actually he has unified logic and calculation in a common symbolism.

- Gottlob Frege (1879) formula language's, that is a lingua characterica, a language written with special symbols, "for pure thought", that is free from rhetorical embellishments... constructed from specific symbols that are manipulated according to definite rules.

- Giuseppe Peano (1888) It's The principles of arithmetic, presented by a new method was the first attempt at an axiomatization of mathematics in a symbolic language.

- Alfred North Whitehead and Bertrand Russell in their Principia Mathematica (1910-1913) has further simplified and amplified the work of Frege.

- Kurt Goëdel (1931) cites the paradox of the liar that completely reduces rules of recursion to numbers.

The concept of algorithm was formalized in 1936 through Alan Turing's Turing machines and Alonzo Church's lambda calculus, which in turn formed the foundation of computer science.
Stephen C. Kleene (1943) defined his now-famous thesis known as the "Church-Turing Thesis". In this context:

" Algorithmic theories... In setting up a complete algorithmic theory, what we do is to describe a procedure, performable for each set of values of the independent variables, which procedure necessarily terminates and in such manner that from the outcome we can read a definite answer, "yes" or "no," to the question, "is the predicate value true?"

Classification

Classification by purpose

Each algorithm has a goal, for example, the purpose of the Quick Sort algorithm is to sort data in ascending or descending order. But the number of goals is infinite, and we have to group them by kind of purposes:

Classification by implementation

An algorithm may be implemeted according to different basical principles.

• Recursive or iterative

A recursive algorithm is one that calls itself repeatedly until a certain condition matches. It is a method common to functional programming. 
Iterative algorithms use repetitive constructs like loops.
Some problems are better suited for one implementation or the other. For example, the towers of hanoi problem is well understood in recursive implementation. Every recursive version has an iterative equivalent iterative, and vice versa.

• Logical or procedural

An algorithm may be viewed as controlled logical deduction. 
A logic component expresses the axioms which may be used in the computation and a control component determines the way in which deduction is applied to the axioms. 
This is the basis of the logic programming. In pure logic programming languages the control component is fixed and algorithms are specified by supplying only the logic component.

• Serial or parallel

Algorithms are usually discussed with the assumption that computers execute one instruction of an algorithm at a time. This is a serial algorithm, as opposed to parallel algorithms, which take advantage of computer architectures to process several instructions at once. They divide the problem into sub-problems and pass them to several processors. Iterative algorithms are generally parallelizable. Sorting algorithms can be parallelized efficiently.

• Deterministic or non-deterministic

Deterministic algorithms solve the problem with a predefined process whereas non-deterministic algorithm must perform guesses of best solution at each step through the use of heuristics.

Classification by design paradigm

A design paradigm is a domain in research or class of problems that requires a dedicated kind of algorithm:

• Divide and conquer

A divide and conquer algorithm repeatedly reduces an instance of a problem to one or more smaller instances of the same problem (usually recursively), until the instances are small enough to solve easily. One such example of divide and conquer is merge sorting. Sorting can be done on each segment of data after dividing data into segments and sorting of entire data can be obtained in conquer phase by merging them.
The binary search algorithm is an example of a variant of divide and conquer called decrease and conquer algorithm, that solves an identical subproblem and uses the solution of this subproblem to solve the bigger problem.

• Dynamic programming

The shortest path in a weighted graph can be found by using the shortest path to the goal from all adjacent vertices. 
When the optimal solution to a problem can be constructed from optimal solutions to subproblems, using dynamic programming avoids recomputing solutions that have already been computed. 
- The main difference with the "divide and conquer" approach is, subproblems are independent in divide and conquer, where as the overlap of subproblems occur in dynamic programming. 
- Dynamic programming and memoization go together. The difference with straightforward recursion is in caching or memoization of recursive calls. Where subproblems are independent, this is useless. By using memoization or maintaining a table of subproblems already solved, dynamic programming reduces the exponential nature of many problems to polynomial complexity.

• The greedy method

A greedy algorithm is similar to a dynamic programming algorithm, but the difference is that solutions to the subproblems do not have to be known at each stage. Instead a "greedy" choice can be made of what looks the best solution for the moment. 
The most popular greedy algorithm is finding the minimal spanning tree as given by Kruskal.

• Linear programming

The problem is expressed as a set of linear inequalities and then an attempt is made to maximize or minimize the inputs. This can solve many problems such as the maximum flow for directed graphs, notably by using the simplex algorithm. 
A complex variant of linear programming is called integer programming, where the solution space is restricted to all integers.

• Reduction also called transform and conquer

Solve a problem by transforming it into another problem. A simple example: finding the median in an unsorted list is first translating this problem into sorting problem and finding the middle element in sorted list. The main goal of reduction is finding the simplest transformation possible.

• Using graphs

Many problems, such as playing chess, can be modeled as problems on graphs. A graph exploration algorithms are used. 
This category also includes the search algorithms and backtracking.

The probabilistic and heuristic paradigm

• Probabilistic

Those that make some choices randomly.

• Genetic

Attempt to find solutions to problems by mimicking biological evolutionary processes, with a cycle of random mutations yielding successive generations of "solutions". Thus, they emulate reproduction and "survival of the fittest".

• Heuristic

Whose general purpose is not to find an optimal solution, but an approximate solution where the time or resources to find a perfect solution are not practical.

Classification by complexity

Some algorithms complete in linear time, and some complete in exponential amount of time, and some never complete.

Algorithms resources on net.

Graph Algorithms in Bioinformatics

Bioinformatics Algorithms Description

Bioinformatics Algorithms Course Page

Bioinformatics Algorithm Demonstrations

Introduction to Bioinformatics Algorithms Lectures 1-2 by Dr. Max Alekseyev USC, 2009

Online Lectures on Bioinformatics

Sequence Alignment Algorithms

Algorithm for sequence alignment: dynamic programming

Network Protocol Analysis using Bioinformatics Algorithms

Bioinformatics Algorithms Links

Dynamic Programming

Particularly good sites...

•http://www.cis.upenn.edu/~sahuguet/MSA/
•http://www.blc.arizona.edu/courses/bioinformatics/align.html
•http://www.cs.monash.edu.au/~lloyd/tildeStrings/Notes/DPA.html
•http://www.cs.orst.edu/~schut/cs325/dynamic.htm
•http://www.catalase.com/dprog.htm
•http://bioweb.ncsa.uiuc.edu/~bioph490/BIOPH2.html#SEQUENCE_COMP
•http://www.qucis.queensu.ca/home/cisc365/javascript/dp1/index.html
Other sites...
•http://bioweb.ncsa.uiuc.edu/~bioph490/dynamic_programming_demo.html
•http://www.qucis.queensu.ca/home/cisc365/365overheads.html
•http://www.qucis.queensu.ca/home/cisc365/dp/dp.p01.html
•http://www.dgp.toronto.edu/csc270/tut_dp.html
•http://queue.ieor.berkeley.edu/~jshu/knapsack/DP/dp.html
•http://mat.gsia.cmu.edu/classes/dynamic/dynamic.html
•http://www.cs.sandia.gov/~scistra/class_3
•http://levine.sscnet.ucla.edu/Econ101/dynamic.htm
•http://mat.gsia.cmu.edu/classes/stoch_dynamic/stoch_dynamic.html
•http://mat.gsia.cmu.edu/classes/dynamic/node8.html
•http://www.maths.mu.oz.au/~moshe/dp/bibl/bibliography.html
•http://cartan.gmd.de/PAPER/ismb95/ismb_html.html
•http://screwdriver.bu.edu/bibliography/dynamic_programming.htm
•http://www.norvig.com/design-patterns/
•http://tome.cbs.univ-montp1.fr/htmltxt/Doc/manual/node137.html
•http://poem.princeton.edu/~verdu/dynamic.html
•http://www.orca1.com/opushelpweb/opusDynamic_Programming.html
•http://screwdriver.bu.edu/cn760-lectures/l7/index.htm
•http://www.ms.unimelb.edu.au/~moshe/dp/dp.html
•http://mat.gsia.cmu.edu/ORCS/0255.html
•http://aae.wisc.edu/e703/notes/a13dynpr.htm
•http://bioweb.pasteur.fr/docs/modeller/node137.html
•http://www2.uwindsor.ca/~lama/my470/ddynamic.htm
•http://students.ceid.upatras.gr/~papagel/project/ex5_6_1.htm
•http://www.cs.sunysb.edu/~algorith/lectures-good/node12.html
•http://www.cs.sunysb.edu/~algorith/lectures-good/node12.html
•http://www.utdallas.edu/~scniu/documents/7315.htm
•http://www.ii.uib.no/~pinar/seminar/larry.html
•http://www.deakin.edu.au/~gecole/books.html
•http://www.cseg.engr.uark.edu/~wessels/algs/notes/dynamic.html
•http://www.csc.liv.ac.uk/~ped/teachadmin/algor/dyprog.html
•http://www.eli.sdsu.edu/courses/fall96/cs660/notes/dynamicProg/dynamicProg.html
•http://www.cs.indiana.edu/l/www/ftp/techreports/TR514.html
•http://www.cs.brandeis.edu/~mairson/poems/node3.html
•http://www.cis.tu-graz.ac.at/igi/oaich/animations/Dynamic2.html
•http://bioweb.ncsa.uiuc.edu/~workshop/

Smith Waterman
•http://genome-www.stanford.edu/Saccharomyces/help/sw_alignment.html
•http://genome-www.stanford.edu/Saccharomyces/help/sw_details.html
•http://www.stanford.edu/~sntaylor/bioc218/final.htm
•http://www.maths.tcd.ie/~lily/pres2/sld009.htm
•http://bioweb.ncsa.uiuc.edu/~workshop/Lab_3/Smith-Waterman.htm
•http://www.tigem.it/LOCAL/SW/threshold.html
•http://sgbcd.weizmann.ac.il/genweb/help/smith-waterman.html
•http://cbrg.ethz.ch/ServerBooklet/section2_3_5.html
Needleman & Wunsch
•http://www.maths.tcd.ie/~lily/pres2/sld003.htm
•http://acer.gen.tcd.ie/~amclysag/nwswat.html
•http://www.nada.kth.se/~erikw/thesis/chapter2_3.html
•http://www.irbm.it/irbm-course95/gb/docs/amps/subsection3_6_1.html
•http://www.ibc.wustl.edu/~zuker/Bio-5495/align-html/node3.html

General (NW vs. SW vs. HMM, etc.)

•http://www.maths.tcd.ie/~lily/pres2/
•http://acer.gen.tcd.ie/~amclysag/nwswat.html
•http://laguerre.psc.edu/biomed/TUTORIALS/SEQUENCE/MULTIPLE/tutorial.html
•http://www.cse.ucsc.edu/research/compbio/

Hmms

•http://www.medmicro.mds.qmw.ac.uk/HMMER/main.html
•http://alfredo.wustl.edu/ismb96/abs/p02.html
•http://www.cse.ucsc.edu/research/compbio/html_format_papers/hughkrogh96/cabios.html
•http://wwwsyseng.anu.edu.au/~jason/hmmlinks.html
•http://www.breadfan.com/markov.html
•http://cslu.cse.ogi.edu/HLTsurvey/ch1node34.html
•http://www.ibc.wustl.edu/service/hmmalign/glocal.html
•http://www.cse.ucsc.edu/research/compbio/html_format_papers/ismb94/node5.html
•http://www.iscs.nus.edu.sg/~luakt/ic3222/lecture/nlp18new/index.htm
•http://www.cse.ucsc.edu/research/compbio/sam.html SAM Software for HMMs

Genetic Algorithms

•http://www.staff.uiuc.edu/~carroll/ga.html
•http://kal-el.ugr.es/gags.html
•http://kal-el.ugr.es/~jmerelo/GAJS.html
•http://www.genetic-programming.org/
•http://www.iitk.ac.in/kangal/deb_tut.shtml

www.cytosolve.com

Post - System Biologist

Job Description: Role of system biology is to design quantitative models of bimolecular networks and to study interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system (Enzyme, metabolites and pathway).

Qualification : B.Tech or M.Sc in Bioinformatics

Required Skills:

1) Basic knowledge of cell signaling pathways, chemical/enzyme kinetics, and differential equation based modeling approach.
2) Previous laboratory experience could be an advantage
3) Good Communication skills.

santhiya.ram@mproductions.com and 044-42946555.

1. Big Data and Bioinformatics

The exponential growth in biological data, driven by advancements in sequencing technologies and high-throughput experiments, has made bioinformatics an indispensable tool. By 2030, we anticipate:

• Petabyte-Scale Data Management: Enhanced storage solutions and cloud computing platforms will allow researchers to handle the vast amounts of data generated from omics studies, including genomics, transcriptomics, and proteomics.

• AI and Machine Learning Integration: Sophisticated algorithms will uncover patterns and relationships in large datasets, enabling predictions about gene function, disease susceptibility, and therapeutic outcomes.

2. Personalized Medicine and Genomics

Bioinformatics will play a pivotal role in tailoring healthcare to individual patients. Key developments include:

• Whole-Genome Sequencing in Clinics: The decreasing cost of sequencing will make it routine in medical diagnostics, enabling personalized treatment plans based on an individual’s genetic makeup.

• Drug Repurposing and Development: Computational tools will identify potential new uses for existing drugs, accelerating the development of targeted therapies.

3. Advancing Computational Tools

The future will see the development of more user-friendly and powerful bioinformatics tools:

• Graph-Based Approaches: Enhanced algorithms for analyzing complex biological networks, such as protein-protein interaction maps.

• Visualization Tools: Intuitive software for visualizing multi-dimensional data, enabling researchers to interpret findings more effectively.

4. Synthetic Biology and Systems Biology

Bioinformatics will continue to drive progress in synthetic and systems biology by:

• Gene Circuit Design: Leveraging computational models to design and simulate synthetic biological systems.

• Understanding Cellular Pathways: Integrating multi-omics data to model cellular processes with unprecedented accuracy.

5. Bioinformatics in Agriculture and Environmental Science

Beyond healthcare, bioinformatics will revolutionize agriculture and environmental conservation:

• Crop Improvement: Genomic studies will help develop high-yield, disease-resistant, and climate-resilient crops.

• Microbial Ecology: Metagenomics will enhance our understanding of microbial communities, aiding in bioremediation and ecosystem management.

6. Democratization of Bioinformatics

Open-source software and accessible education will broaden participation in bioinformatics research:

• Community-Driven Projects: Collaborative platforms like GitHub will continue to foster innovation in tool development.

• Education and Training: Online courses and workshops will bridge skill gaps, enabling researchers from diverse backgrounds to contribute.

Challenges and Ethical Considerations

While the future is bright, challenges remain. Data privacy and ethical concerns surrounding genetic information require careful navigation. Furthermore, addressing the digital divide is critical to ensuring equitable access to bioinformatics resources globally.

Conclusion

The future of bioinformatics is boundless, with opportunities to revolutionize our understanding of life and improve human health. As technologies evolve and collaborations flourish, bioinformatics will undoubtedly remain at the forefront of scientific discovery, unlocking the secrets of life one dataset at a time.

A walk-in Interview for the following position tenable at the Bioinformatics Infrastructure Facility (BIF), Department of Biotechnology, Alagappa University will be held at the Department of Biotechnology, Science Campus, Alagappa University, Karaikudi 630 004 on 03.08.2013 (Saturday) at 12:30 PM. This national facility is funded by the Department of Biotechnology, Ministry of Science and Technology, Government of India, New Delhi. The main objectives of the Centre involve teaching and research activities in bioinformatics/biotechnology.

1. Studentship (One Post):

Stipend : Rs. 5000 p.m. (consolidated)

Qualification: M.Sc., in Bioinformatics/Biotechnology/Biophysics/Biochemistry/
Life Sciences

Interested candidates are encouraged to send their Curriculum Vitae by email to alagappauniv.btisnet@nic.in in advance. On the day of interview, the candidates must produce original certificates in proof of their educational qualification and experience and a recommendation letter from the Head of the Department/Institution where last studied/worked. Candidates who have already passed the required Degree alone are eligible to appear for interview. No TA&DA will be given for attending the interview.

Advertisement: http://www.alagappabiotech.org/Notification.pdf

1️⃣ Data Analysis for the Life Sciences
A fantastic starting point to learn statistics, R programming, and exploratory data analysis in the context of biology. The best part? It’s available free online from HarvardX.

2️⃣ Practical Computing for Biologists
The very first book I picked up when I started learning computational biology. It’s beginner-friendly and focuses on essential computing skills every biologist needs.

3️⃣ A Primer for Computational Biology
An open-access, hands-on introduction to computational biology concepts and coding techniques. Perfect if you want to learn through real examples.

4️⃣ Computational Genomics with R
For those who already know R and want to dive deeper into genome-scale data analysis, from sequence alignment to gene expression.

5️⃣ The Biologist’s Guide to Computing
Bridges the gap between biological problems and computational thinking, making it easier for life scientists to approach programming and data analysis.

6️⃣ Bioinformatics Data Skills
A must-read to sharpen your bioinformatics toolkit — from command-line skills to reproducible research workflows. Ideal once you’ve covered the basics.

7️⃣ Bioinformatics Workbook
A practical tutorial series to help scientists design bioinformatics projects, analyze data, and understand best practices.

8️⃣ Modern Statistics for Modern Biology
An essential guide to modern statistical methods applied to biology, blending theory with hands-on examples in R.

9️⃣ Algorithms on Strings, Trees, and Sequences by Dan Gusfield
A classic reference for anyone wanting to understand the algorithms behind sequence alignment, genome assembly, and biological data structures.

People using computers were often seen
as helpers, not leaders—
useful, but not essential.

Sometimes, the criticism was direct.
Sometimes subtle.
But the message was the same—
this work doesn’t really count.

Then biology changed.
The questions became bigger,
and experiments alone
were no longer enough.

Organizing knowledge by hand worked once.
Now it needs computers—
to handle scale, speed, and complexity.

Some patterns are simply invisible
if you look at one sample.
You need many—
and the right tools to understand them.

So we started building maps—
of genomes, cells, and systems.
Not perfect,
but extremely useful.

Ideas also had to become clearer.
It’s no longer enough to say something sounds right—
you have to measure it.

The divide between “types” of biologists
never really made sense.
We are solving the same problems—
just in different ways.

Progress didn’t wait for agreement.
It moved forward with data,
with code,
and with careful analysis.

What matters now is simple:
• Biology depends on computation
• Coding is an important skill
• Statistics helps us think clearly
• And the people building these tools
are shaping the future of science

1. Accelrys Software Solution Pvt Ltd.
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2. Apticraft Systems (P) Ltd.
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www.apticraft.com

3. Aptuit Informatics
Plot No. 100-103, Export Promotion Industrial Park, White Field, Bangalore-560066
www.aptiuit.com

4. Bigtec
J. K. Towers, 8th Block, Sangam Circle,46th Cross, Bangalore-560082.
www.bigtec.org

5. Bijam Biosciences Private Limited
Nagarjuna Hills, Hyderabad 500 082, India
www.nagarjunagroup.com

6. Bio Base Databases India Pvt Ltd.
Crescent Towers, 4th Floor, No : 32/1, Crescent Road, Bnagalore – 560 001
www.biobase-international.com

7. BioImagene India Pvt. Ltd.
4th floor, C-Wing, Godrej Eternia, Shivajinagar, Pune-411005
www.bioimagene.com

8. BioInformatics Institute Of India – Noida
C-56 A/28, Sector -62, Noida – 201 301
www.bii.in

9. CLC bio India Pvt Ltd
#Plot No. 51, H.No. 8-3-214/51, Srinivasa Nagar (West) Ameerpet Hyderabad – 500 038
www.clcbio.com/india

10. CytoGenomics India (P) Ltd.
#3004, 12A Main HAL 2nd Stage, Bangalore 560008
www.silicocyte.com

11. Genotypic Technology
211, 6th Cross, 80ft Road, RMV II Stage, Bangalore 560094
www.genotypic.co.in

12. Genvea Biosciences
Dr. D. T. Singh, CSO, 53, Craig Rd. #04-01, Singapore-089691
www.genvea.com

13. Helix Info Systems
132 A, II Floor, Sterling Towers, IV Cross Street, Sterling Road, Nungambakkam, Chennai.
www.helixinfosystems.com

14. Jalaja Technologies Pvt. Ltd.,
21/1,Victoria Layout, Victoria Road, Bangalore-47
www.jalaja.com

15. Jubilant Biosys Ltd
#96, Industrial Subrub, 2nd Stage, Yeshwanthpur, Bangalore- 560022
Jubilant Organosys Ltd.
1A, Sector 16A, Noida – 201 301 (India)
www.jubl.com

16. Kshema Technologies
#1, Global Village, Mylasandra, Mysore Road, Bangalore-560 059.
www.mphasis.com

17. LabNetworx
B-704, Gitanjali Apartments, Vikas Marg Extension, New Delhi – 110 092
www.labnetworx.com

18. LabVantage Solutions Pvt. Ltd.
Bengal Intelligent Park, Building C, 2nd Floor, Sector V, Salt Lake Electronics Complex, Kolkata – 700 091
www.labvantage.com

19. LeadInvent, 
2nd Floor, Biotech Centre, University of Delhi, South Campus, Benito Juarez Road, New Delhi 110021, India
Contact no: +91 11 24119241
Email: contact@leadinvent.com
www.leadinvent.com

20. Mascon Life Sciences
B – 8/ 10, Vasant Vihar, New Delhi 110057, India
www.masconlifesciences.com

21. Molecular Connections P Ltd
Kandala Mansion, 2/2 Kariappa Road, Near Krishna Rao Park, Basavangudi, Bangalore – 4
www.molecularconnections.com

22.Novo Informatics Pvt. Ltd.
TBIU, 2nd Floor, Synergy Building, Indian Institute of Technology, Hauz Khas, New Delhi-16.
Contact: 91-11-26581524, 91-11-26581766(Extension: 28)
Email: info@novoinformatics.com
www.novoinformatics.com

23. Ocimum Biosolutions (India) Ltd
6th Floor, Reliance Classic, Road No.1 Banjara Hills, Hyderabad 500 034, India.
www.ocimumbio.com

24. Scube Scientific Software Solutions
613, Hemkunt Chambers, 89, Nehru Place, New Delhi -110 019
www.scribeindia.com

25. Siri Technologies Pvt Ltd.
38/C -23, South End Road, Basavanagudi, Bangalore-56004.
www.siritech.com

26. Strand Life Sciences Pvt. Ltd.
#237, Sir C. V. Raman Avenue, Raj Mahal Vilas, Bangalore 560 080 INDIA
www.strandls.com

27. SooryaKiran Bioinformatics (P) Ltd
TBIC-13, Tejaswini Building, Technopark, Thriruvananthapuram- 695 584, Keralam, India

Ph: +91 471 4060979,+91 9895404104
Email: reachus@sooryakiran.com
http://www.sooryakiran.com

28. Systat Software Asia Pacific
4th Floor, Block 1, Shankar Narayan Building, No.25, MG Road, Bangalore – 560001
www.systat.com

29. ABC Genomics (India) Pvt. Ltd.
Biotech Park, Sector G, Jankipuram, Kursi Road, Lucknow-226021, U.P., INDIA
Tel +91-522-4068579, Email: director@abcgenomics.com
www.abcgenomics.com

30. en-GENE-ier's Core Technology Services,
1/340, Virat Khand, Gomtinagar, 
(Near Maharaja Agrasen Public School)
lucknow-226010, U.P., India.
http://www.bio.egicore.com/

Best of luck for your job hunts :).