Address of the bookmark: https://github.com/b-brankovics/grabb

Essential Qualifications: Ph.D. (Bioinformatics/ Biophysics/ Biotechnology or any other stream of biological/ physical sciences) with a minimum of two publications in reputed peer reviewed journals in the area of structural bioinformatics or biophysics or biomolecular modeling/ simulation.

Job description: Development of bioinformatics tools and algorithms/software for structure based analysis of biomolecular systems. Programmatic access to major biomolecular databases using APIs Knowledge based prediction and analysis of biomolecular structure, function and interactions. Docking/simulations for inhibitor design.

Desirable Qualifications (Research Associate/s): i) Strong computer programming skills (in Python/PERL/PHP or C++ or object oriented database management systems like MySQL etc or scripting languages under LINUX/UNIX environment).

ii) Extensive experience in computational analysis of biomolecular structure/interactions and usage of advanced biomolecular simulation softwares. iii) Adequate knowledge of major databases, webservers and softwares in the area of biomolecular structure/function and drug design. iv) Familiarity with Parallel Programming environments and experience in usage of high-end HPC clusters.

The candidates must highlight their experience in above mentioned fields/topics in their CV. Initial appointment will be for a period of 1 year, subject to extension after review of performance.

Emoluments: As per DST, GOI norms and commensurate with experience.

More at https://www.iisc.ac.in/positions-open/

The fellowship will be for a period of 3 years, or for a period of 4 years, with 25 % compulsory work (e.g. teaching responsibilities at the department) contingent on the qualifications of the candidate and the teaching needs of the department.

Starting date no later than October 1, 2021.

More at https://www.jobbnorge.no/en/available-jobs/job/199984/phd-research-fellow-in-genomics-and-bioinformatics

Affiliation
The selected candidate will be affiliated to one U.S. host institution.

USIEF strongly recommends all applicants to identify institutions with which they wish to be affiliated, and to correspond, in advance, with potential host institutions. If the applicant has secured a letter of invitation from a U.S. institution, it should be included as a part of the online application. The letter of invitation should indicate the duration of visit, preferably with dates.

Grant Benefits

These fellowships provide J-1 visa support, a monthly stipend, Accident and Sickness Program for Exchanges per U.S. Government guidelines, round-trip economy class air travel between India and the U.S., a modest settling-in allowance, and a professional allowance.

Subject to availability of funds, a dependent allowance and international travel may be provided for one accompanying eligible dependent provided the dependent is with the grantee in the U.S. for at least 80 per cent of the grant period. Flex grantees are not eligible for dependent benefits.

Eligibility Requirements

In addition to the General Prerequisites:

Applicants must have a Ph.D. degree within the past four years. S/he must have obtained a Ph.D. degree between September 15, 2018 and September 14, 2022. The applicants are required to upload his/her Ph.D. degree certificate/provisional Ph.D. certificate on the online application;
Applicants must have a publication in reputed journals and demonstrate evidence of superior academic and professional achievement. S/he must upload a recent significant publication (copy of paper/article) on the online application (not exceeding 30 pages); and
If applicant is employed, please follow the instructions carefully regarding employer’s endorsement. If applicable, obtain the endorsement from the appropriate administrative authority on the Letter of Support from Home Institution. The employer must indicate that leave will be granted for the fellowship period. The applicant can download the Letter of Support from Home Institution from the USIEF website. Candidates working under government-funded projects are also required to get endorsement from their affiliating institutions in India.

How to Apply

Applications must be submitted online at: https://apply.iie.org/fvsp2023/

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.

Louisiana Biomedical Research Network (LBRN) announces registration for it's Summer 2019 Bioinformatics Training Program. The program will be focused on processing, analysis and interpretation of next generation sequecning data for biologists. Learn more:

https://edu.t-bio.info/louisiana-biomedical-research-network-summer-2019-lbrn-bioinformatics-training-program/

Address of the bookmark: https://bioinformatik.de/en/bioinformatics-in-germany/research/research-groups.html

https://bioalgorithms.ucsd.edu/research4.html

An IVOM approach exploits compositional biases using variable order motif distributions and captures more reliably the local composition of a sequence compared to fixed-order methods. Optionally the predictions can be parsed into a 2-state 2nd order Hidden Markov Model (HMM), in a change-point detection framework, to optimize the localization of the boundaries of the predicted regions. The predictions (embl format) can be automatically loaded into the freely available Artemis genome viewer.

Address of the bookmark: http://www.sanger.ac.uk/science/tools/alien-hunter