1. Work to establish improved quantitative experimental assays (such as microarrays or differential in-gel electrophoresis) and
2. Development of modern computational methods (such as hierarchical probabilistic models or integration of heterogeneous data sources)

Lab page @ http://bioinf.boku.ac.at/

Age Limit: 35

How to Apply for the AIIMS Life Science Job:

Interested applicants are asked to send out a detailed CV to Dr Ashok Sharma (aioncoaiims@gmail.com). Laboratory of Chromatin and also Cancer Epigenetics, Department of Biochemistry with the subject line “Application for Scientist-B position for MeitY project” latest by January 01st, 2021.
Complete Information of the year of passing, experience, marks, etc. ought to be mentioned in the CV Incomplete. applications will certainly be rejected Just shortlisted applicants will be called for interview. Chosen candidates will certainly be intimated by email/phone.
No TA/DA will certainly be paid for appearing in the interview.
Note, The institute reserved the right to fill up or not to fill up the post advertised.

Emoluments: Rs. 56,000/- plus 24 percent HRA

Eligibility:
2nd class Master’s Degree with a PhD in a pertinent subject (Bioinformatics) from.a recognized University
1st class Master’s degree in Life Sciences (Bioinformatics) from a recognized university OR.
Bachelor’s Degree in Engineering or-Technology with minimal 60% marks from a recognized University or equivalent.

Desirable Qualifications:
Experience in Bioinformatics/NGS data. Analysis/System Biology/Computer Science/ statistics with experience in Machine learning/Al project.
Experience of Deep learning applications in biological data ( image/text).
Proficient in Rf Python machine learning libraries.
Prior experience in the cancer-related project (ML-based) will be advantageous.
Experience with PyTorch/TensorFlow will certainly be very desirable.
Applicant should have strong scientific writing as well as. verbal abilities.
Papers in sci-indexed journals demonstrating ML skill sets.
Database handling will certainly be plus yet not required.

More detail at https://www.aiims.edu/images/pdf/recruitment/advertisement/biochem-16-12-20.pdf

Please add ur links/bookmarks in comment section.

Address of the bookmark: http://physicsdatabase.com/free-math-books/

Additional Information:

If you have any questions about scientific aspects of this position, please contact Prof. Lars Bertram, head of LIGA (lars.bertram@uni-luebeck.de).

Please contact Ms. Anna Wolbert for further questions about administrative details (recruiting@uksh.de).

Weitere Informationen erhalten Sie auch unter www.uksh.de/karriere.

Wir freuen uns auf Ihre Bewerbung bis zum 15.03.2020 unter Angabe unserer Ausschreibungsnummer 20190570.119.CL.

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/23/14/1713.full

More at https://genohub.com/ngs-instrument-guide/

Address of the bookmark: https://genohub.com/ngs-instrument-guide/

CENTRE FOR BIOINFORMATICS

PUDUCHERRY

Applications are invited for one Project Assistant to work in the UGC sponsored Research Award "Molecular Docking and Dynamics studies to understand the interacting mechanism of oncogenic 101 protein with its cellular proteins".

The duration for the fellowship is 12months only with consolidated pay ofRs. 5,000 per month.

Application on plain paper with following details: Name, Address, Data of Birth, Father's Name, Nationality, Educational Qualification (SSLC onwards-enclose attested copies of certificate) and Researcb Experience may be addressed to Dr. R. Krishna, Principle Investigator (PI), UGC Research Award, Centre for Bioinformatics, Pondicherry University, Pondicherry - 605 014.

Application should reach in January 261h , 2013.

Essential Qualification: M.Sc. in Bioinformatics/Biophysics with good academic record.

Qualification for Project Fellow:

M.Sc in Bioinformatics/Biophysics.

The person to be considered for appointment as Project Fellow must have second class master degree with a minimum of 55% marks in the subject concerned or a related subject.

The candidate to be appointed as Project Fellows should be below thc age of40 years at the time of appointment.

Desirable Qualification for this Project: Research Experience in Small/Macromolecule Crystallography and Structural Bioinformatics.

For more details, refer the web site: www.pondiuni.edu.in/sites/default/files/BIC-311213.pdf

(Under Tenure-Track Model)

Open Advt. No IIITA/DIC/16012014

IIIT-Allahabad has several Openings for the Faculty positions at the Assistant Professor level.

It is a regular tenure-track faculty positions for 3-5 years in teaching and research. A regular faculty is expected to engage heavily in research and teaching. The eligibility criteria for regular faculty positions are similar as in IITs. For an Assistant Professor position, a candidate must have a PhD (in IT/Computer Science &/or Engineering/Electrical, Electronics &/or Communication Engineering/etc; for interdisciplinary areas the PhD may be in an appropriate field), plus three years experience. However, for PhDs from a well known University/Institute (e.g. IITs/IISc/TIFR/ISI in India or well known research universities across the world), and a good research/academic record, the 3 years experience requirement may be waived.

The pay scale for faculty is same as in IITs. Other benefits include initiation research grant, travel support, book grant, professional society membership, etc., and personal benefits such as medical/LTC, on campus subsidized family housing with excellent modern infrastructural facilities.

Areas of Interest

IIIT-Allahabad aims to build strong research groups in important and emerging areas in CS/IT/ECE as well as in emerging interdisciplinary areas, and applications are invited in all these areas. Some of the areas of special interest, besides strengthening the existing research areas, are : Software Engineering, Theoretical Computer Science, Cyber Physical Systems, Robotics, Network science, Digital Media, Computational neuroscience, Machine learning, Healthcare informatics, Computational Biology, Communications networks (both at hardware and protocol levels), Circuits (including VLSI, analog, low power, etc), Energy systems and technologies, Biomedical electronics and systems, Computer Architecture, signal/image processing, Embedded and control systems.

Application Process

Interested candidates can apply by sending their detailed CV with list of publications clearly mentioning Journal names and citation index with three references through email entitled “Faculty positions at IIIT Allahabad” to faculty.applications@iiita.ac.in. Do not send your applications in any other email addresses. Applications will be considered regularly, hence there is no deadline for applying.

Important Clarifications on Eligibility

A PhD in CS/IT (or other disciplines, as announced) is the minimum expected requirement for an Assistant Professor.

Advertisement: http://iiita.ac.in/pub/Faculty-Position-IIITA1.pdf

A recent study published in Genome Biology (https://link.springer.com/article/10.1186/s13059-026-04280-y) addressed this challenge by introducing dicast (https://github.com/burgshrimps/dicast), a machine-learning approach for detecting structural variants in short-read sequencing data. Structural variants, such as large deletions, insertions, duplications, and inversions, can have significant biological and clinical implications, yet they are challenging to identify with short-read technologies. Because different detection methods frequently yield divergent results, researchers commonly employ consensus calling, considering a variant valid only if multiple tools detect it. While this approach reduces false positives, it relies on the potentially flawed assumption that the majority is always correct.

The researchers explored the impact of evaluating the supporting evidence for each variant, rather than simply tallying the number of algorithms that identified it. To establish a ground truth, they analyzed nine genomes using multiple sequencing technologies and 15 detection methods, initially identifying approximately 35 million potential variants. Through extensive filtering, evidence integration, and manual review of over 11,500 variants, they developed a robust benchmark comprising more than 236,000 structural variants. The findings underscored the complexity of the problem: short-read methods detected fewer than half of deletions and less than 10 percent of insertions, with performance declining markedly in repetitive genomic regions. In contrast, long-read technologies demonstrated superior detection capabilities. However, replacing the substantial volume of existing short-read data in clinical and research settings is not immediately feasible. Consequently, the researchers questioned whether short-read data might harbor more information than conventional analytical pipelines currently extract.

This line of inquiry led to the development of dicast. Rather than merely confirming agreement among multiple tools, dicast identifies patterns in sequencing data, including split and clipped reads, discordant read pairs, alignment characteristics, and the surrounding genomic context. An XGBoost machine-learning model evaluates which combinations of these signals are indicative of genuine structural variants. Thus, the approach shifts from tallying algorithmic consensus to interpreting the underlying evidence.

The researchers subsequently conducted a targeted evaluation by examining structural variants detected by only a single short-read tool, which are typically missed by consensus-based approaches. dicast successfully recovered approximately 81% of these single-caller deletions, insertions, and duplications. The signals for these variants were present in the data, but conventional filtering methods failed to integrate them effectively.

The utility of dicast was further demonstrated in cohorts with rare diseases, including congenital limb malformations, atrial fibrillation, and neuromuscular disorders. In one instance, dicast achieved a deletion recall rate of approximately 0.96, compared to 0.74 using consensus calling. The median number of variants requiring manual review was 29 per sample. Among 31 experimentally validated variants that standard filters would have missed, dicast identified 12, whereas consensus calling detected only one.

Overall, dicast identified approximately 20 percent more potential disease-causing deletions than consensus-based methods. While a 20 percent increase may appear modest, in clinical genomics such improvements can have significant practical implications. Missing a deletion may leave a case unresolved, whereas detecting a structural variant can provide critical diagnostic insights.

The study does not claim that machine learning has rendered short-read sequencing superior to long-read approaches. Instead, the results underscore the effectiveness of long-read sequencing for structural variant detection. However, dicast highlights a more nuanced perspective: substantial biological information may still be recoverable from the extensive short-read datasets already available.

The principal lesson extends beyond the detection of structural variants. For many years, bioinformatics pipelines have relied on threshold-based criteria, such as minimum coverage, quality scores, or support from multiple tools. While these rules are useful, biological phenomena do not always conform to rigid checklists; multiple weak signals, when considered collectively, can provide compelling evidence.

This perspective prompts consideration of the solitary variant: one algorithm identifies it, while several others do not. Traditional consensus techniques might have dismissed it, yet machine learning approaches evaluate the available evidence to determine whether the variant is plausible.

Occasionally, the most significant variant within a genome is the one that is almost universally overlooked.

Read more at https://link.springer.com/article/10.1186/s13059-026-04280-y