(Deemed University)

Hamdard Nagar, New Delhi – 110 062

R E C R U I T M E N T

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Applications on prescribed form are invited for filling up the following teaching positions in the Department of Biotechnology, Faculty of Science in the university. Eligible candidates may apply on or before 30.09.2014.

1. Professor/Associate Professor - One in Pay Band of Rs. 37400-67000+ AGP Rs.10000/9000

2. Assistant Professor - Two in Pay Band of Rs. 15600-39100+ AGP Rs. 6000/-

ASSISTANT PROFESSOR – 02 (including 01 SFS)

Specialization : Bioinformatics

Qualification and Experience :

Ph.D. in Biotechnology or an allied discipline with M.Sc. in Biotechnology/ Biochemistry in the First division or equivalent grade from a recognized University/ Institute.

NET in Life Science or allied discipline in addition to the above qualification.

Experience : At least two years of Post-doctoral teaching and/or research experience in Bioinformatics or relevant field in a UGC recognized Institution of repute or international research institute/ University. Proof of research to be evidenced by publications in SCI-indexed journals of high impact factor as the first or corresponding author.

Note : University may consider exempting candidates from NET, who has been awarded Ph.D. degree from ‘A’ Grade accredited University following the procedure as notified by the UGC in its Regulations of 2009 and adopted by Jamia Hamdard.

For more information: http://www.jamiahamdard.ac.in/PDF/Online%20application%20form%20_Teaching_1.pdf
http://www.jamiahamdard.ac.in/PDF/PBAS.pdf

1. MSc. or B. Tech. or equivalent degree in Biotechnology or related life sciences discipline.
2. Strong aptitude for laboratory work and should be detail-oriented person.
3. Hands-on experience in basic molecular biology techniques.
4. Prior experience in working in a research laboratory or industry.
5. Basic IT skills that include familiarity with Microsoft Office packages.
6. Ability to carry out basic maintenance of general lab equipments and laboratory resources.
7. Ability to maintain accurate records of laboratory work.
8. Willingness to learn, and should be a team player.
Desirable Experience and Skills:
1. Familiarity with NGS technology.
2. Experience in preparation of NGS libraries.
3. Familiarity with Sanger sequencing technology (capillary electrophoresis based)

Remuneration: Remuneration will commensurate with expertise and experience.

How to Apply: Interested applicants fulfilling the criteria may send their detailed CV and a cover letter that explains their suitability for this position, in a single PDF, to Dr. Sreekanth Reddy at careers_bioit@ibab.ac.in. Last date for submission of application is 23rd February 2019. Please mention the position applying for in the subject line of the email.

About IBAB: The Bio-IT Centre at IBAB has state-of-art sequencing facility with the HiSeq 2500 and accessories such as Qubit, Covaris, Agilent 2200 TapeStation, Stratagene Mx 3000 for next generation sequencing, 3500 Dx Genetic Analyzer for capillary electrophoresis based sequencing, and HiScan for microarray imaging. The facility is fully operational and providing services to the scientific community. The Institute of Bioinformatics and Applied Biotechnology (IBAB) is a unique institute engaged in education, research and entrepreneur support programs and is based at Electronic City, Bangalore. IBAB’s mission is to catalyze the growth of the biotechnology and bioinformatics industries in India. To know more please visit: http://www.ibab.ac.in/index.php/bioit/

Functional and Comparative Genomics

http://jp.senescence.info/
http://www.senescence.info/
http://www.liv.ac.uk/integrative-biology/staff/joao-de-magalhaes/

In addition to the knowledge of the human genome, all these genomes, widely sampled across mammals, can be used to research how particular organisms respond to different conditions. Some otters, for example, have a thick, water-resistant shell, and some rodents, but not all, have adapted to hibernation. These animal traits will help us to understand human traits, such as metabolic diseases.

With climate change and more animal ecosystems being threatened by human activity, the protection of endangered species is becoming increasingly important. Scientists have historically researched several people in various populations of a species to understand the genetic variation that occurs in that species. This is important for understanding how particular species can be protected. In this study, animals on the Red List of Endangered Species of the International Union for Conservation of Nature had fewer differences in their genomes, which is consistent with their endangered status.

Ref @ A comparative genomics multitool for scientific discovery and conservation https://www.nature.com/articles/s41586-020-2876-6

 Data at http://zoonomiaproject.org/

Extent of employment: 30 Hours per Week
Duration of employment: 1st of October 2015 to 30th of September 2019
Gross monthly salary and pay grade in terms of collective agreement for university staff (payable 14 times per year): B1, € 1.997,20

Responsibilities
The successful candidate (f/m) will pursue a Ph.D. project related to the interpretation of plant genome and transcriptome sequencing data from next-generation sequencing (NGS) platforms. In particular, the candidate will characterize the unexplored genome of quinoa, a crop plant of long-standing tradition in Latin America. We collaborate with research partners in Austria and abroad, and the candidate’s project will be of central importance in the context of this research network.

Required skills and qualifications
We are looking for a graduate student (f/m) with a Master’s degree in bioinformatics or in a related field, solid programming skills (e.g. developing sequence analysis tools), experience with the analysis of NGS data sets, understanding of lab methods and knowledge of genomics/transcriptomics. The group has successfully performed several projects using NGS technology. We have recently published the reference genome sequence of sugar beet (Dohm et al., Nature, 2014), a crop plant closely related to quinoa (same family, but different genus). Not yet published is a quinoa genome assembly that we have generated, and which will serve as the starting point of the candidate’s project. We are a multidisciplinary team and offer work in a lively and friendly atmosphere, and state-of-the-art computing infrastructure. We are looking forward to expanding our team by a dedicated and strongly motivated person with a distinct interest in the challenges of plant genomics.

Applications can be submitted until: 16th of August 2015

University of Natural Resources and Life Sciences Vienna seeks to increase the number of its female faculty and staff members. Therefore qualified women are strongly encouraged to apply. In case of equal qualification, female candidates will be given preference unless reasons specific to an individual male candidate tilt the balance in his favour.

Please send your job application (incl. letter of motivation, CV, summary of Master’s thesis and contact details for two referees) to Personnel department, University of Natural Resources and Life Sciences, 1190 Vienna, Peter-Jordan-Straße 70; E-Mail: kerstin.buchmueller@boku.ac.at. (Reference code: 59)

We regret that we cannot reimburse applicants travel and lodging expenses incurred as part of the selection and hiring process.

www.boku.ac.at

• A simple intro to statistical distributions
• hypothesis testing
• linear models.

reading: http://compgenomr.github.io/book/stats.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/compgen2021_stats.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/

Module 2: Unsupervised learning for genomics (9-15 August 2021)

• Understanding basic intuition behind machine learning approaches.
• Using unsupervised learning to cluster and visualise data points
• Dimension reduction techniques for visualisation and as input to clustering methods

reading: http://compgenomr.github.io/book/unsupervisedLearning.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/compgen2021_unsupervisedLearning.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/

Module 3: Supervised learning for genomics (16-22 August 2021)

• Understanding and using supervised learning methods for predictive purposes
• How to measure prediction performance
• Understand and use cross-validation and related concepts

reading: http://compgenomr.github.io/book/supervisedLearning.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/compgen2021_supervisedLearning.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/

https://github.com/BIMSBbioinfo/compgen2021

Address of the bookmark: https://github.com/BIMSBbioinfo/compgen2021

• Updated human gene set (GENCODE 21)
• Updated rat gene set including manual annotation from HAVANA
• New species: Vervet-African green monkey
• Imported Transcript Support Levels (TSLs) from UCSC for human and mouse
• Imported APPRIS flag for human and mouse
• Updated Amazon molly gene set

Find more at http://www.ensembl.info/blog/2014/10/02/ensembl-77-has-been-released/

More at https://www.igib.res.in/bdmg/ScientistRecruitmentAdvt2023.pdf

Orange Bioinformatics provides access to publicly available data, like GEO data sets, Biomart, GO, KEGG, Atlas, ArrayExpress, and PIPAx database. As for the analytics, there is gene selection, quality control, scoring distances between experiments with multiple factors. All features can be combined with powerful visualization, network exploration and data mining techniques from the Orange data mining framework.

Address of the bookmark: https://pypi.python.org/pypi/Orange-Bioinformatics/2.5.34

The Power of Evo 2: AI Meets DNA

Evo 2 is the largest AI model for biology ever created, trained on an astonishing 9.3 trillion DNA "letters" (nucleotides) carefully selected from genomes spanning the entire tree of life. This massive dataset ensures that Evo 2 can recognize patterns and relationships in genetic sequences at an unparalleled scale.

For the first time, scientists can design DNA with AI, moving beyond simple sequence analysis to active DNA generation. Evo 2 enables researchers to predict, modify, and even create entire genetic sequences, opening new possibilities in medicine, agriculture, and synthetic biology.

Decoding the Dark Genome

One of the biggest challenges in genetics is understanding the non-coding regions of DNA—vast stretches of the genome that do not code for proteins but play crucial roles in regulating gene expression. These regions control when and how genes are activated, influencing everything from development to disease.

Evo 2 is designed to decode these non-coding elements, helping researchers uncover their functions and use this knowledge to develop gene-based therapies, synthetic life forms, and precision agriculture solutions.

From Reading DNA to Writing It

To put Evo 2’s impact into perspective:

• Previous AI models could "read" DNA like a book, analyzing genetic sequences and identifying patterns.
• Evo 2 can "write" entirely new DNA, designing functional genes, chromosomes, and even full genomes from scratch.

This means scientists can now engineer biological systems with AI, designing new proteins, metabolic pathways, and genetic circuits to address real-world challenges.

A Step Toward Generative Biology

The Arc Institute describes Evo 2 as a major step toward "generative biology"—a revolutionary approach where AI is used to create novel biological structures rather than just analyzing existing ones. This could lead to breakthroughs such as:

• New medicines: AI-generated enzymes and proteins tailored for targeted therapies.
• Disease-resistant crops: Genetically optimized plants for higher yield and climate resilience.
• Synthetic organisms: Custom-designed microbes for bioremediation, biofuel production, and industrial applications.

An Open-Source Revolution

Unlike many proprietary AI models, Evo 2 is open source, making its capabilities accessible to researchers worldwide. This democratization of AI-driven biology means that scientists from different disciplines can collaborate, experiment, and innovate, accelerating discoveries in genetic engineering and synthetic biology.

With Evo 2, the boundaries of what’s possible in DNA design, genetic engineering, and biological innovation are being redrawn. The future of life sciences is no longer just about understanding life’s code—it’s about writing it.