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Berkeley Lab is Bringing Science Solutions to the World, and YOU can be a part of it!

In the world of science, Lawrence Berkeley National Laboratory (Berkeley Lab) is synonymous with "excellence." That's why we hire the best -- whether in research, finance or other operations. This is a great opportunity to bring your top-notch skills to bear in support of world-class scientific research that addresses national and global challenges!

Position Summary:
The Department of Energy Joint Genome Institute (DOE JGI, a division of Berkeley Lab) is looking for a Research Scientist to join the Metagenome Program. The position will report to the Metagenome Program Group Lead.

This position has three primary roles:
* Design, conduct and publish independent research that pushes the boundaries of metagenomics. Current research cuts across ecosystems and includes projects like discovering new RNA viruses in metagenomes through machine learning methods, sequencing the first large-scale soil virus metagenomes, constructing large scale Bayesian ecological networks and exploring plant-microbe interactions.
* Develop new computational and molecular methods in metagenomics that can be applied by our production groups to thousands of metagenome samples per year.
* Assist scientists who have received user grants from the Joint Genome Institute in planning and implementing experiments.

Specific Responsibilities:
* Lead or assist in diverse 16S tag, metagenome and metatranscriptome sequencing efforts.
* Perform new and detailed analysis of ribosomal DNA sequence data, metagenomic shotgun sequence data, and metatranscriptome data: build phylogenetic trees, analyze annotated metagenomes, and perform comparative analyses.
* Work on the development of software for machine learning and network analysis of genomic data.
* Design and carry out experiments aimed at recovering microbial genome sequence from environmental samples.
* Create, design, and execute experiments to obtain metagenomic, metatranscriptomic and/or single cell genomic data from target functional and phylogenetic groups of interest
* Perform Research & Development experiments to improve and automate various aspects of metagenomic sequencing, assembly, annotation, and binning.
* Identify areas of improvement and efficiency and offer feedback.
* Troubleshoot all stages of experimental process and optimize Standard Operating Procedures (SOP).
* Interface with collaborators, JGI staff, management, and sponsors at other national laboratories.
* Publish in peer-reviewed journals; contribute to scientific research papers and reports.
* Prepare proposals.
* Lead, mentor and train postdoctoral fellows, technical staff and other group members as necessary.

Additional Desired Responsibilities:
* Attend group meetings and present status reports and scientific findings.
* Keep an accurate and detailed record of laboratory and analytical work.

Required Qualifications:
* Ph.D. in Molecular Biology, Biology, Genetics or a closely related discipline and/or normally less than 5 years related
* Demonstrated ability to independently carry out creative research with a proven record of publications and achievement
* Experience programming in Python, Bash scripting and familiarity with Linux/Unix
* Experience with environmental nucleic acid extraction and next generation sequencing technologies and with the R statistical analysis platform
* Familiarity with Molecular Biology Techniques such as: PCR, qPCR, cloning
* Familiar with command line sequence analysis tools e.g. Bbtools, Last, Hmmer, Megahit, Velvet, Samtools
* Familiar with databases, e.g. RefSeq, BioCyc, PFAM, IMG
* Experience in 16S rRNA sequence analysis tools and databases including QIIME, Greengenes, RDP, and Silva
* Demonstrated ability to conduct and perform collaborative research and effectively interact with a broad range of colleagues with tact and diplomacy
* Effective problem solving and decision-making skills with the ability to troubleshoot experimental processes and provide analysis
* Excellent organizational, analytical, and record-keeping skills with the ability to organize and present technical reports to collaborators, JGI staff, management, and sponsors
* Demonstrated ability to accurately and eloquently represent and promote scientific projects to audiences of diverse technical backgrounds
* Effective interpersonal skills with experience establishing effective collaborations and interacting with members of the scientific, instrumentation and informatics communities
* Excellent written communication skills with demonstrated experience preparing funding proposals and research publications

Additional Desired Qualifications:
* Experience with machine learning especially using using Apache Spark/MLlib, Scikit-learn and deep neural networks
* Experience in viral biology

Work will be performed at the DOE Joint Genome Institute (JGI) in Walnut Creek, CA.

This is a 1 year, career-track term appointment that may be renewed to a maximum of 5 years and that may be converted to career based upon satisfactory job performance, continuing availability of funds, and ongoing operational needs. This position requires completion of a background check.

Salary is commensurate with experience.

Berkeley Lab addresses the world's most urgent scientific challenges by advancing sustainable energy, protecting human health, creating new materials, and revealing the origin and fate of the universe. Founded in 1931, Berkeley Lab's scientific expertise has been recognized with 13 Nobel prizes. The University of California manages Berkeley Lab for the U.S. Department of Energy's Office of Science.

Apply directly online at and follow the on-line instructions to complete the application process.

The posting shall remain open until the position is filled.

Equal Employment Opportunity: Berkeley Lab is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status. Berkeley Lab is in compliance with the Pay Transparency Nondiscrimination Provision under 41 CFR 60-1.4 ([link]). Click here ([link]) to view the poster: "Equal Employment Opportunity is the Law".

Submitter October 2, 2016
Seattle, WA, USA

In conjunction with the ACM Conference on Bioinformatics, Computational Biology and Health Informatics 2016

BigLS is a workshop series dedicated to the broad theme of Big Data in life sciences. The workshop is focused on, but not limited to, the following themes:

* Scalable algorithms and techniques for big data analytics in molecular biology
* Statistical and integrative approaches to big data biology
* Emerging Machine Learning and AI techniques for big data biology
* High performance computing methods and software for big data biology
* Software and hardware foundations for managing big data in biomedical informatics

Cross-topic papers focusing on techniques for heterogeneous medical data, translational research big data and P4 medicine are especially welcome!

This year, the workshop will feature a keynote address by Nathan Price, Professor and Associate Director of the Institute for Systems Biology.

Thanks to the support from the NSF, the workshop will offer the travel grants for students and postdoctoral researchers from US academic institutions.

For paper submission instructions and other details, please visit the workshop web site at:

Paper submission: June 24, 2016
Authors notification: July 15, 2016
Camera-ready papers: July 29, 2016

The Knowledge Systems Group at Dana-Farber Cancer Institute seeks a Senior Computational Biologist to perform integrative cancer genomics analysis and build cloud-based computational pipelines. This is an essential role for building precision medicine platforms within DFCI, analyzing genomic profiles and patient cohorts from TCGA and DFCI's enterprise sequencing effort, and contributing to our recently announced partnership with Intel for the Collaborative Cancer Cloud (CCC).


* Perform integrative cancer genomics analysis on multiple data sets, including TCGA and the DFCI Profile project.
* Build cloud-based computational pipelines for genomic analysis within the Collaborative Cancer Cloud (CCC) project.
* Perform predictive modeling and distributed machine learning to correlate genomic signatures with patient outcome on large-scale cancer genomic data sets.
* Collaborate with a group of computational biologists and software engineers to integrate your analysis into new precision medicine platforms at DFCI.

Required Skills:
* Ph.D. in computational biology or computer science, with a strong cancer genomics focus
* Expertise in integrative cancer genomics analysis
* Expertise in building and validating NGS pipelines, such as variant calling pipelines or copy number analysis pipelines
* Excellent communication and writing skills
* Exceptional attention to detail
* Strong leadership skills and high-level of persistence to lead a scientific project from start to finish

Desirable Skills:
* Prior experience in analyzing TCGA data sets
* Prior experience in building cloud-based computational pipelines. Experience with WDL or CWL a plus.
* The candidate must also demonstrate outstanding personal initiative and the ability to work effectively as part of a team.

Located in Boston, Dana-Farber Cancer Institute brings together world renowned clinicians, innovative researchers and dedicated professionals, allies in the common mission of conquering cancer, HIV/AIDS and related diseases. Combining extremely talented people with the best technologies in a genuinely positive environment, we provide compassionate and comprehensive care to patients of all ages; we conduct research that advances treatment; we educate tomorrow's physician/researchers; we reach out to underserved members of our community; and we work with amazing partners, including other Harvard Medical School-affiliated hospitals.

Email a short description of your interest with your resume / CV directly to: Ethan Cerami: cerami AT

Equal Employment Opportunity:
Dana-Farber Cancer Institute is an equal opportunity employer and affirms the right of every qualified applicant to receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status, or other groups as protected by law.

Submitter EXCERPT:
It's no secret that Google has broad ambitions in healthcare. But a document obtained by New Scientist reveals that the tech giant's collaboration with the UK's National Health Service goes far beyond what has been publicly announced.

The document -- a data-sharing agreement between Google-owned artificial intelligence company DeepMind and the Royal Free NHS Trust -- gives the clearest picture yet of what the company is doing and what sensitive data it now has access to.

The agreement gives DeepMind access to a wide range of healthcare data on the 1.6 million patients who pass through three London hospitals run by the Royal Free NHS Trust -- Barnet, Chase Farm and the Royal Free -- each year. This will include information about people who are HIV-positive, for instance, as well as details of drug overdoses and abortions. The agreement also includes access to patient data from the last five years.


September 3, 2016, 9:00 - 17:00
The Hague, The Netherlands
World Forum, room: t.b.c.

Submit an abstract for ECCB 2016 workshop -- extended deadline 18th May

W2 -- Network Inference: New Methods and New Data

Mammalian systems constitute over 200 cell types, each specialized to perform a distinct function, and yet all cell types share the same genome. This cell type specificity is achieved by a context-specific interpretation of the DNA sequence to produce a cell type specific transcription signature. Advances in sequencing techniques have accelerated the characterization of transcription landscapes across many normal and malignant cell types. The challenge now is to integrate these data to understand transcriptional control at a systems level. Over the years, powerful machine learning algorithms have been developed for inferring transcriptional networks from expression data, thereby revealing new aspects of complex biological systems.

This one day SIG session will bring together experts from computational biology and machine learning to present recent advances in the development and application of gene regulatory network inference methods, as well as novel emerging single-cell and epigenomics data types suitable for network inference. The SIG will be split into two half day sessions. The first half will focus entirely on novel network inference methods, while the second half will focus on opportunities and challenges arising from new data types. Each session will feature an invited speaker and three short talks.

The target audience is researchers working in the field of network inference or anyone who is working with large scale genome wide data. We expect typically 20 participants.

Six talks will be selected by the organisers from submitted abstracts (max. 250 words). There will also be a 'hands-on' session with short pitches of new network inference tools or databases. Abstracts for both kinds of talks should be submitted online via the Easychair submission system: [link]. The deadline for submission is 18 May 2016.


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