The Role

We are looking for a Software Engineer - Computational Biologist with 3+
years' experience in a similar role to design and implement a backend system
to support an online individualised genomics interface. This position is a
great opportunity for an ambitious, self-motivated individual to work in a
demanding, challenging and interesting role.

Position Description:
. Participate in planning, design, and implementation of Equinome back
end systems and technologies.
. Implement interfaces and management tools for back end services.
. Manage, analyse, interpret and visualise large genomics data sets.
. Work closely with scientific team to develop new features and
application enhancements
. Design, develop and manage a genomics research database.

Qualification/Experience:
. Minimum MSc in Computer Science, Genetics, Bioinformatics or in a
related field (A Ph.D qualification would be an advantage).
. Proven 3+ years of experience in similar role.
. Highly proficient in Python, SQL, MySQL.
. Excellent knowledge of mammalian genomics, bioinformatics and
statistical/population genetics.
. Hands-on experience working with large data sets.
. Experience with front-end technologies (HTML/CSS/Javascript) an
advantage.
. Experience in rapid web application development: e.g. Django.
. Knowledge or experience of Unix Scripting and R statistical
programming language would be an advantage.
. Ability to work with minimum supervision to deliver high-quality
code on time.
. Fluency in English and good written and communication skills.
. Meticulous attention to detail.

Applications should be submitted before Friday, 26 September 2014 using the
following link:
http://bit.ly/WgbhxS

Note: Full information and application procedure is available at this link:
http://bit.ly/WgbhxS

We work in a highly interdisciplinary environment at the interface of Computer Science and Biology. Since its inception, our lab has eagerly engaged in collaborative research partnerships with biological and experimental collaborators, facilitated by our affiliation with the Broad Institute and the Computational and Systems Biology initiative (CSBi) at MIT, our participation in the Epigenome Roadmap, ENCODE, and modENCODE consortia, and by several other ongoing collaborations at MIT, Harvard, and the Harvard Medical School affiliated hospitals.

http://compbio.mit.edu/

Main research topics:
- Comparative and Population Genomics of Plant Symbionts
- Parasite Genome Evolution
- Experimental Evolution of Microbial Symbionts and Parasites
- Phylogenomics of Early Branching Fungi

More at http://corradilab.weebly.com/

• Automatically improve draft assemblies
• Find variation among strains, including large event detection

Pilon requires as input a FASTA file of the genome along with one or more BAM files of reads aligned to the input FASTA file. Pilon uses read alignment analysis to identify inconsistencies between the input genome and the evidence in the reads. It then attempts to make improvements to the input genome, including:

• Single base differences
• Small indels
• Larger indel or block substitution events
• Gap filling
• Identification of local misassemblies, including optional opening of new gaps

More at https://github.com/broadinstitute/pilon/wiki

Address of the bookmark: https://github.com/broadinstitute/pilon/wiki

Models and software for predicting who is at risk of carrying genetic variants that confer susceptibility to cancer. Application to breast, ovarian, colorectal, pancreatic and skin cancer.

Statistical methods for the analysis of high throughput genomic data: analysis of cancer genome sequencing projects; integration of genomic information across technologies; cross-study validation of genomics results.

Statistical methods for comparative effectiveness research: comprehensive models for lifetime history of chronic disease outcomes; Bayesian meta-analysis; Bayesian causal inference; decision analysis.

Bayesian modeling and computation: multilevel models; decision theoretic approaches to inference; sequential experimental design and their application to adaptive and multistage studies in clinical and epidemiological research.

http://bcb.dfci.harvard.edu/~gp/index.html

http://scholar.google.com/citations?user=OlpYP3UAAAAJ&hl=en

More at https://leidosbiomed.csod.com/ats/careersite/jobdetails.aspx?site=4&c=leidosbiomed&id=2040

Now my main interests are in gene regulation, where we work on promoter analysis; non-coding RNA, where miRNAs and structure prediction are the main areas; and protein structure, where the group is working on methods for structure prediction from sequence. To read more about these topics, please see the research pages.

Lab page @ http://wiki.binf.ku.dk/User:Krogh

The course Bioinformatics Algorithms (Part 1) by Pavel Pevzner, Phillip E. C. Compeau, and Nikolay Vyahhi from University of California, San Diego will be offered free of charge to everyone on the Coursera platform. Sign up at http://www.coursera.org/course/bioinformatics.