Post Research Officer, NTRF, Kolkata

Essential Post Graduate degree in Life Science having special paper in Bioinformatics. Post Graduate degree in Bioinformatics.

Desirable Ph. D. in the area of computational biology or bioinformatics.

Job Experience Work experience on database development, capable to work independently and efficient in bioinformatics related project.

Job Responsibility

To develop database on various aspects of tea research.

Screening of the In-house projects of NTRF at the preliminary level.

Review and evaluation of In-house projects of NTRF. Technical monitoring of the In-house projects of NTRF.

Reviewing the final technical report of the In-house projects of NTRF.

Putting-up of same proposals for taking approval and sanction of the competent authority.

Physical and technical verification of the In-house projects of NTRF.

To assist in organizing seminars / workshops / meetings etc.

Interested candidate may appear for walk-in Interview on 17 th November, 2016

More Info :

http://www.teaboard.gov.in/pdf/Recruitment_for_the_post_of_Research_Officer_pdf7538.pdf

Calls

Events

Projects

Jobs

Address of the bookmark: http://www.dbtindia.nic.in/out-reach/latest-announcements/

Applicant are required to bring applications on plain paper, stating the name, address, date of birth. educational qualifications and experiences and Institute, along with attested photocopies of mark sheets and certificates etc. at the time of Interview. Applications should also be sent by Entail to Dr. Madhu Chopra, Coordinator, BIF Facility. Email: acbrdu.blisnet@nic.in; mchopradu@gmail.com.
Interview Date : 13.02.2017
Time : 10:00 am-12:00 noon
Venue : ACBR

Applications are invited for:

Junior Research Fellow, in a DBT funded project, is available in Translational Health Group, ICGEB, New Delhi

Qualifications:

Education: M.Sc. (preferably in Biotechnology, Life Sciences or Zoology, Chemistry, Bioinformatics). Candidates with hands on experience on GC-MS data acquisition and analysis will be given preference. Bioinformatics expertise required.

Fellowship: As per DBT guidelines.

Tenure: The position is purely on temporary basis with an initial tenure of six months and based on satisfactory performance may continue until the completion of the project.

Closing date for applications: 04/03/2017

Please send a "TWO PAGE" CV by email to: th.icgeb@gmail.com on or before the last date.

Research Associate, in a DBT funded project, is available in Translational Health Group, ICGEB, New Delhi

Qualifications:

Education: Ph.D. (in Biology, Biotechnology, Chemistry, Bioinformatics). Candidates with hands on experience on GC-MS data acquisition and analysis will be given preference.

Fellowship: As per DBT guidelines.

Tenure: The position is purely on temporary basis with an initial tenure of six months and based on satisfactory performance may continue until the completion of the project.

Closing date for applications: 04/03/2017

Please send a "TWO PAGE" CV by email to: th.icgeb@gmail.com on or before the last date.

Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent or Master’s in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent with 4 years or 5 years of Bachelor’s degree having 1st Division or 60% marks or equivalent overall grade point average, with at least two years of research experience as evidenced from fellowship/ associateship/ training/ other engagements.

Knowledge in System Biology/ Statistical and computational Genomics/ Bioinformatics
Knowledge in computer programming, LINUX OS.
Expertise in use of R/other Bioinformatics software

More at http://iasri.res.in/employment/2017/cabin_advertisement_RA_SRF_YP_Mar2017.pdf

Phenomics of Moisture Deficit Stress Tolerance and Nitrogen Use December 31, 2019

Research Associate (RA) Two (2)

Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent or System Administrator/ Computer expert for database development, development of phenome data bank and virtual phenomics facility, data archiving and Efficiency in Rice and Wheat-Phase II (Funded by National Agricultural Science Fund, ICAR) Master’s in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent with 4 years or 5 years of Bachelor’s degree having 1st Division or 60% marks or equivalent overall grade point average, with at least two years of research experience as evidenced from fellowship/ associateship/ training/ other engagements. maintenance; Development of image analysis algorithms, APIs and IAPs.

Knowledge in System Biology/ Statistical and computational Genomics/ Bioinformatics
Knowledge of programming in LINUX/R/Perl/JAVA/PHP/JSP and use of various software & tools.
December 31, 2019

Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science / Computer Application or equivalent or Master’s in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent with 4 years or 5 years of Bachelor’s degree having 1st Division or 60% marks or equivalent overall grade point average, with at least two years of research experience as evidenced from fellowship/ associateship/ training/ other engagements.

Knowledge of Statistical and Computational Genomics/ Bioinformatics.
Knowledge of programming in LINUX/R/Perl/JAVA/PHP/JSP and use of various software & tools.
March 31, 2020

Also, Bioinformatics Industrial Training Program (BIITP) is merged with the HRD Biotechnology Industrial Training Programme (BITP).

While this comes as a surprise for a lot of participants. We believe this is a good attempt to unify and create a national benchmark for talent. And we appreciate this endeavor from Department of biotechnology.

However, such last-minute announcements can create confusion. Thus candidates are advised to go through the complete notification DBT-BET JRF 2019 via the link below.If you have any kind of doubts, you must contact DBT JRF or Biotecnika for any kind of help & assistance.

Attention:-Bioinformatics Programs (BINC and BIITP)

1. Bioinformatics National Certification (BINC) has been merged with DBT-Junior
Research Fellow (BET Exam)

2. Bioinformatics Industrial Training Program (BIITP) is merged with HRDBiotechnology Industrial Training Programme (BITP).

Students of Bioinformatics, who are interested to apply for Fellowship or Industrial
Training may keep track of the advertisement of DBT-JRF (BET Exam) and BITP
of DBT.

 More at http://www.bcil.nic.in/files/Attention_Bioinformatics_Programs_(BINC_and_BIITP).pdf

1. Learn and get updated

Some of the bad biological programmers only learn new technical or non-technical things when it’s absolutely necessary. The good biological programmers learn new technical skills proactively. But great biological programmers not only learn new technical skills on their own but also learn non-technical skills, and have an open mind to sources of knowledge that others may shut out.

In other concrete term, the bad biological programmer learn Perl's regular expression when they started a project on comparative genomics; the good biological programmer learned it a year before because it looked interesting; and the great biological programmer also read about the BioPerl packages, genomics, DNA string, genomic theories, or some similar course of study so that they could understand the results and explain it biologically.

2. Not a merely coder!!!

I often encountered with biological programmer who call themself a hard-core computer programmer and avoid biology. I can almost guarantee that if you are one of them then you are not doing research but merely writing "dry" codes.

According to my supervisor most of the computational biologist, don't know what they are doing biologically. Even they struggle to explain their own programs output and results. Therefore, It is highly advisable to learn basic of biology which can assist you to explain the result and understand your discovery. Always remember you are a researcher not a coder.

3. Be Social with biologist

The computational biologist spends most of the time in from of computers, writing codes. They always think their job is to produce working codes, not technical research perfections. But, they are completely wrong. You should not forget that apart from your computational skills you also need some biologist, other than your supervisor, to explain and make you understand the complex biological mechanism.

I highly recommend your to interact with biotech researchers and learn how do they explain their one graph (which they generally produce after one year of work) biologically. Remember, the origin of your research project is complex biological phenomenon, which is more complex than that of your limited programming rules.

4. Do not search, research for answers

Researching for answers means more than typing several keywords into a search engine or posting a question at Stack Overflow or the BioStars forums. I have entered problems into search engines that generate no results, and every question I posted on Stack Overflow or the BioStars forums never got anything resembling an answer, yet I solved the issues and moved on. I’m not a magician — I just know how to find answers or discover root causes.

Many problems are situational, and if you depend on search engines and forums, you can waste a lot of time going down a rabbit hole and possibly never getting a solution. Learn to perform root cause analysis, learn enough about the underlying system to look for other clues and solutions, and learn to take a long distance view of an issue before deep diving into it.

5. Love and defend your research

You cannot rise to the top in this research profession without loving your work. There are some very good “it’s just a job” biological programmers (I’ve been one at times), but if that is your outlook, you won’t be willing to do whatever it takes to succeed. This idea gets a lot of folks in a huff, because they feel it is a personal insult. “I’m a good programmer, but I have other priorities and can’t make work my life.” I understand completely; I have other priorities too. As much as I hate to say it, when I am passionate about my work, I am willing (though not eager) to abandon my other priorities to finish the job. It is not an insult to say that if you aren’t willing to pull out all the stops you can’t be the best, it is a fact.

You must be passionate about more than programming — you must also be excited about your research, the tools and technology you are using, and so on. I have seen very good and even great biological programmers operating at mediocre levels because something was not a good fit, such as they hated the project or were using a technology they disliked. Therefore, like your research project and get excited about your discoveries. You have not only to discover but also defend your finding with scientific words.

Thanks to all of you for reading.

MEME Suite software development; gene expression; mathematical modelling; gene regulation and transcription

Specialization:
Pattern recognition and modelling in computational biology

Link @ http://www.imb.uq.edu.au/tim-bailey

Applications are invited for the STUDENTSHIP and TRAINEESHIP vacancies to carry out project/research work in the DBT - Bioinformatics Infrastructure Facility with consolidated stipend of Rs.5,000/- per month.

Essential Qualification

Student Trainee: Those who have completed M.Sc., Bioinformatics/Biophysics/Life sciences or Pursuing M.Tech., Bioinformatics/Biotechnology

Duration : 3-4 Months

Student Trainee: Those who are pursuing M.Sc Bioinformatics/Biophysics/ Life sciences/others

Duration : 2-3 Months

Mail your CV on or before 25th November 2013 to shirai2011@gmail.com and hard copy to "Dr. D. Velmurugan, Professor & Head, CAS in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai 600 025". Also, the applicants are requested to attend the interview on 29th November, 2013 at 11 A.M.

Advertisement:

www.unom.ac.in/uploads/announcements/bifadvertisement_20131114080003_23240.pdf

The most common newbie questions are:

Should I try to use these free open source programs?  Why are we not trying GUI software for computational analysis? Should I use commercial bioinformatics programs/software?”

1. Let’s be open

We generally think free and cheap are useless. But this concept is not applicable when we discuss open source software. Mostly, the bioinformatics software is developed by highly competitive biological programmers who believe in open sharing of knowledge. They come under Open Bioinformatics Foundation or O|B|F which is a non-profit, volunteer run organization focused on supporting open source programming in bioinformatics. The best part about open source tools/software is that they’re free to download the source code and read exactly what the program does. If you are so inclined, you can view all of the parts of the program and see the logical flow of the pipeline. In addition, open source makes an excellent learning tool for any beginning bioinformatician. Moreover, you can modify existing open source programs to deal with cutting-edge problems or to customize your pipeline. Apart from your computational and analysis work, most of the reviewer also prefers the open source based results so that they can validate the results if validation required.

2. Code headache

As a bioinformatician you are supposed to know the basics of programming languages, and if you are not good at it, then please learn it as soon as possible because you are not a bio-analyst but biological programmers. The open source programs usually lack dedicated service and support teams (often because they were the product of an overworked doc/postdoc!) so you are responsible for troubleshooting your own errors most of the time. We commonly receive the HELP email to support and assist to setup the pipeline; you can also find this kind of request on any QA forum. I personally believe this coding horror brings the biggest downside of open-source programs; where you need some programming skills in order to implement the program in your pipeline. But, if you are not able to fix the pipeline and modify the open source code according to your requirements them you should re-think on your bioinformatician name tag!!!

3. Dive into the codes

Some of the biologist turn bioinformatician says “if you can do the same thing with commercial software then why to get migraine with weird codes”, well this statement looks to me that guys are keen to learn swimming but still don’t like to get wet. If you are still using paid software and doing your work by customer support and clicking some of the well-designed GUI button then perhaps you are not interested in learning and trying new and challenging bioinformatics works. You are missing the basic flavour of bioinformatics. Let’s dive into the coding world, I am sure your will enjoy it. I recommend your to swim freely in code’s sea, and enjoy the journey; do not merely watch it from the outside.  

4. Paid does not mean better

The bioinformatics company which are specializes in bioinformatics solutions develop well designed/packed, user friendly software by using a large number of specialised scientist, programmers and support staff. They also provide good services to accomplice your biological analysis work. This means that if you hit a ‘snag’ with your data, help is likely only a phone call away! These companies price their products competitively against the cost of a dedicated bioinformatician. You may be able to afford the program, but not the additional staff! Additionally, most of the functionality that you need in your analysis is already coded into the program. Need to plot a graph? Just click this button right here. It is that easy. But, as a bioinformatician this is not generally well encouraged approach in biological analysis work, because the software is not available to everyone and your data can’t be validated. Moreover, there is very less chances that anyone will repeat your work or love to do similar kind of research (because not all the labs in the world are rich like yours).

5. Take a caution

In biological analysis work, in which you deal GB/TB of data are having maximum chances of getting errors, so please be careful and always cross check your data before coming to any conclusion. Even an error in two line code can alter your entire analysis and display weird results. Some of the scientist blindly believes on commercial software, which is entirely wrong. Using proprietary tools does not absolve you of the need to actually read and research the type of analysis that you are doing. This is particularly true in the case of genome assembly and annotation.

At the end, I would like to tell only one think that open source solutions allows you to do more cutting edge analysis than the commercial tools. So let’s go for it.

Disclaimer:

This is my personal view. I have nothing to do with any company or open source community. The views expressed on these pages are mine alone and not those of my current/past employers. I do reserve the right to remove comments left by spammers or off-topic comments.