Bioinformatics.org
[OMICtools]
[Repositive.io]
Not logged in
  • Log in
  • Bioinformatics.org
    Membership (40390+) Group hosting [?] Wiki
    Franklin Award
    Sponsorships

    Careers
    About bioinformatics
    Bioinformatics training
    Bioinformatics jobs

    Research
    All information groups
    Online databases Online analysis tools Online education tools More tools

    Development
    All software groups
    FTP repository
    SVN & CVS repositories [?]
    Mailing lists

    Forums
    News & Commentary
  • Submit
  • Archives
  • Subscribe

  • Jobs Forum
    (Career Center)
  • Submit
  • Archives
  • Subscribe
  • All groups - News

    Latest announcements

    September 11-14, 2018
    National Institutes of Health
    9000 Rockville Pike
    Building 60, Room 162
    Bethesda, MD 20892, USA
    faes.org/node[...]p;d4=

    OBJECTIVES

    The participants will be provided with end-to-end hands-on training, along with introduction to basic concepts, in using popular tools and techniques for sequence analysis, structure analysis, function prediction, biological database searching, "omics" data analysis, pathway analysis, data visualization, data curation and integration, linux, R, perl and scripting basics.

    Hands-on Skills/Tools taught:
    • Databases: NCBI-ENTREZ, UniProt, PDB, STRING, others
    • Sequence analysis and function predictions: EMBOSS suite & others
    • Local Alignment: EMBOSS-WATER
    • Global Alignment: EMBOSS-NEEDLE
    • Similarity search: NCBI BLAST, PSI-BLAST
    • Multiple sequence alignment: Clustal Omega, MUSCLE, MAFFT
    • Phylogenetics: MrBayes, MEGA, FigTree and Dendroscope
    • Motif finding, analysis: MEME suite
    • Structure prediction, visualization & analysis: PyMOL, Chimera, iTASSER
    • Transcriptome analysis: NCBI GEO, Tuxedo tools, R
    • Enrichment analysis: DAVID
    • Pathway analysis: Cytoscape
    • Programming: Linux, R, Perl, Python
    • Platforms: EMBOSS, UGENE, H2O, Galaxy

    Submitter

    BACKGROUND

    Paramount are working in partnership with Genomics England to expand their team in order to deliver success with the 100,000 Genomes Project. This is a challenging and fast moving project with the aim to carry out whole genome sequencing on 100,000 participants.

    RESPONSIBILITIES

    We are excited to advertise this position for a Senior Rare Disease Analyst as a 12-month fixed contract with the likelihood of extension. This senior role is part of a talented and motivated Bioinformatics team and it will be responsible for applying computational and statistical methods to analyse large datasets using:
    • Whole exome/genome sequencing
    • Association testing
    • Rare variant association analysis
    • Burden testing
    • Benchmarking genome analysis pipelines

    REQUIREMENTS

    Experience Required:
    • Post doctorate with at least 2-3 years' experience of working in this field
    • Deep knowledge of association testing
    • Good knowledge of genomics and rare diseases
    • A demonstrable ability to cope under pressure and deliver to deadlines
    • Ability to communicate effectively within a multidisciplinary team
    • Flexible and co-operative approach to colleagues
    • Ability to work independently and to show initiative within a team
    • Ability to prioritise and balance competing demands
    • Excellent technical writing skills

    COMPENSATION

    There are some great benefits on offer with this role including a competitive salary, pension, generous holidays and more.

    ABOUT US

    Genomics England works at the cutting edge of science, technology and healthcare. Our mission is to deliver the ground-breaking 100,000 Genomes Project −- the biggest national genome sequencing project of its kind anywhere in the world. As it moves beyond the 100,000 Genomes Project, Genomics England will work with NHSE to launch the world's first Genomic Medicine Service within a national healthcare system. In partnership with government, the NHS, academia, industry and the public, Genomics England aims to realise the potential of genomic medicine: to embed state-of-the-art care in the NHS; bring health benefits to UK citizens; and consolidate the UK's position as the 'go to' destination for international genomic research and investment.

    HOW TO APPLY

    Please do not hesitate to contact Harvey Uppal at huppal[at]pararecruit.com or call (+44) 121 616 3407 to discuss this opportunity further.

    Keywords: Rare, Disease, Analyst, WGS, WES, Genomics, Bioinformatics, Pipelines, Statistics, Testing, Writing, Variants, NGS, London, Fixed-Term.

    BACKGROUND

    About TGen:
    Translational Genomics Research Institute (TGen) is a Phoenix, Arizona-based non-profit organization dedicated to conducting groundbreaking research with life changing results. TGen is focused on helping patients with neurological disorders, cancer, and diabetes, through cutting edge translational research (the process of rapidly moving research towards patient benefit). TGen physicians and scientists work to unravel the genetic components of both common and rare complex diseases in adults and children. Working with collaborators in the scientific and medical communities literally worldwide, TGen makes a substantial contribution to help our patients through efficiency and effectiveness of the translational process. TGen is allied with City of Hope, a world-renowned independent research and cancer and diabetes treatment center. This precision medicine alliance enables both institutes to complement each other in research and patient care, with City of Hope providing a significant clinical setting to advance scientific discoveries made by TGen. For more information, visit: www.tgen.org.

    Dr. Nicholas Schork, Director of Quantitative Medicine at TGen, seeks a creative, independent, and highly motivated researcher interested in computational and general quantitative aspects of health and clinical learning systems (i.e., systems that can be used to make predictions about intervention strategies for individual patients based on large clinical and outcome data sets). Clinical learning systems have the potential to radically transform the way health care is delivered and monitored and have their root in large-scale electronic medical record systems, artificial intelligence and machine learning techniques and clinical decision support strategies.

    RESPONSIBILITIES

    The post-doctoral fellow will have the opportunity to develop and evaluate health learning systems in a collaborative environment that includes basic scientists, computational and quantitative scientists, informaticians and clinicians, including those at TGen's affiliated institution, the City of Hope Hospital in East Los Angeles, as well as its many other partner institutions.

    Learning systems in health care contexts take advantage of large amounts of data on patients (e.g., from Electronic Medical Records (EMRs)) to build predictive models that can be applied to future patients to determine sensible, if not optimal, courses of action based on their characteristics (e.g., genetic profile, past medical history, etc.). Such systems can be updated in real time given the continual patient accrual in clinical care settings. The design and implementation of such systems is receiving greater and greater attention, but present a number of challenges. For example, how can one harmonize relevant data in real time? What statistical analysis techniques should be used to develop the predictive models (e.g., deep learning)? How can such systems be coordinated across clinical entities to leverage even more data? The research questions and activities to be pursued in these contexts include:
    • Modeling and simulating the behavior of real-time clinical learning systems
    • Developing reliable, beyond state-of-the-field data-based analytic methods for guiding clinical decisions
    • Comparing different machine learning and statistical predictive analytic methods for their flexibility, robustness and reliability on simulated and real data
    • Developing strategies to integrate a wide variety of data types (e.g., genomics, imaging, wireless devices, etc.)
    • Beta-testing the implementation of novel systems with potential industry and academic partners
    • Helping oversee the implementation of systems and consulting on any issues that arise
    • Present findings on learning systems at major conferences and in peer-reviewed publications
    TGen has access to some of the largest and fastest computing systems in the world that can be leveraged in the proposed research. In addition, the development of health learning systems will likely have a broad, international impact, so the research to be pursued will be highly visible and competitive and as a result could propel the career of an independently-minded researcher looking to impact others on a large scale.

    REQUIREMENTS

    • Ph.D. (completed or near completion) in Computer Science, Operations Research, Informatics, Statistics/Biostatistics, Mathematics or a related field.
    • Exceptional computer programming skills
    • Familiarity with state-of-the-field machine learning and statistical analysis techniques
    • Experience with large, heterogeneous data sets
    • Excellent written and verbal communication skills
    • Willingness to work in clinical decision-making settings
    • Willingness to travel when necessary to collaborating sites
    • No publication record is required, but a publication track record is preferred

    HOW TO APPLY

    If you are interested in seeing the most up to date job listings or to apply for this position, please go to www.tgen.org/care[...]9e6Uk and submit your resume for IRC13069. Please take a moment to read about our employee benefits and learn more about TGen. If you have any questions about the company or how to apply for a position, please contact hr[at]tgen.org.

    Only resumes submitted through the TGen career website will be considered.

    BACKGROUND

    About TGen:
    Translational Genomics Research Institute (TGen) is a Phoenix, Arizona-based non-profit organization dedicated to conducting groundbreaking research with life changing results. TGen is focused on helping patients with neurological disorders, cancer, and diabetes, through cutting edge translational research (the process of rapidly moving research towards patient benefit). TGen physicians and scientists work to unravel the genetic components of both common and rare complex diseases in adults and children. Working with collaborators in the scientific and medical communities literally worldwide, TGen makes a substantial contribution to help our patients through efficiency and effectiveness of the translational process. TGen is allied with City of Hope, a world-renowned independent research and cancer and diabetes treatment center. This precision medicine alliance enables both institutes to complement each other in research and patient care, with City of Hope providing a significant clinical setting to advance scientific discoveries made by TGen. For more information, visit: www.tgen.org.

    Dr. Nicholas Schork, Director of Quantitative Medicine at TGen, seeks a creative, independent, and highly motivated researcher interested in developing and applying novel and comprehensive annotation methods for genes and genetic variants in a wide variety of contexts, in particular disease diagnosis, therapeutic choice and cross-species (orthology) disease model analyses. The methods to be developed will consider modeling the influence of genetic variants on gene function and the functional effects of genes that can be reconciled across species.

    RESPONSIBILITIES

    The post-doctoral fellow will have the opportunity to develop and evaluate computationally-based gene and genetic variant functional models in a collaborative environment that includes basic scientists, computational and quantitative scientists, biomedical technology developers, evolutionary biologists and clinicians, including those at TGen's affiliated institution, the City of Hope Hospital in East Los Angeles, as well as its many other partner institutions.

    Variant functional annotation and orthology assessment methods are being developed rapidly. In addition, databases harboring information about the likely influence of genes and genetic variants in pathway and network contexts exist and continually being refined (e.g., the GTEX databases). Finally, large-scale functional studies using emerging model systems and in vitro assays such as CRISPR screens are being pursued to generate information that needs to be mined and reconciled. The challenge will be to integrate all of this into more comprehensive models of gene function and leverage them to make claims about the health status of individuals, the likely benefits of specific gene-targeting therapeutics, and the utility of specific model systems to understand disease pathogenesis. In this context, the proposed activity and research will involve:
    • Modeling and the influence of genetic variants on gene expression, protein structure and function, splicing, and other molecular physiologic processes
    • Considering the combined influence of multiple genetic variants on gene function
    • Developing models that consider compensatory and feedback mechanisms in networks and biological processes
    • Developing methods that leverage sequence orthology and homology across different species to assess function
    • Contributing to the development of software and webtools that implement the models to be developed
    • Participating in the implementation and evaluation of the application of the models to be developed
    • Present the models at major conferences and in peer-reviewed publications
    TGen has access to some of the largest and fastest computing systems in the world that can be leveraged in the proposed research. In addition, the development of the proposed models will likely have a broad, international impact, given the amount of attention genetics and genomics research is receiving, so the research to be pursued will be highly visible and competitive, and as a result could propel the career of an independently-minded researcher looking to make an indelible impact on a biomedical science.

    REQUIREMENTS

    • Ph.D. (completed or near completion) in Bioinformatics, Systems Biology, Computational or Quantitative Biology, Biostatistics, Comparative Biology, or a related field.
    • Computer programming, R, scripting languages, and biological database query skills
    • Familiarity with genomics and comparative biology
    • Experience with large biological data sets
    • Excellent written and verbal communication skills
    • Willingness to work in collaborative settings
    • No publication record is required, but a publication track record is preferred

    HOW TO APPLY

    If you are interested in seeing the most up to date job listings or to apply for this position, please go to www.tgen.org/care[...]9e6Uk and submit your resume to IRC13068. Please take a moment to read about our employee benefits and learn more about TGen. If you have any questions about the company or how to apply for a position, please contact hr[at]tgen.org.

    Only resumes submitted through the TGen career website will be considered.

    RESPONSIBILITIES

    Mission:
    Fournir l'expertise en analyse et traitement de données et garantir la qualité des analyses statistiques. Les analyses concernent principalement les données générées à haut débit au sein de l'Unité Technologique Génomique et Transcriptomique, avec des objectifs multiples (ex: recherche de signatures, recherche de biomarqueurs, analyse de réseau de régulation, analyse métagénomique, etc) en intégrant si nécessaire des données biologiques/cliniques multiples (ex : analyse de survie, longitudinale, analyse multi-omiques).

    L'ensemble des activités permettant le bon fonctionnement de l'Unité sont réalisées en interaction forte avec les autres membres de l'équipe – ingénieur(s), technicien(s) en biologie moléculaire et bioinformaticien(s).

    Principales Activités:
    • Conseiller/définir des designs expérimentaux adaptés dans le cadre des projets de recherche BIOASTER
    • Choisir/développer et mettre en place les méthodes mathématiques, statistiques et bioinformatiques nécessaires à l'analyse des données biologiques (séquençage nouvelle génération, puce à ADN, qPCR, ...)
    • Choisir/développer et mettre en place des méthodes de prédiction de biomarqueurs (prédictifs, diagnostiques, pronostiques, de réponse au traitement) à partir de données biologiques issues de données mono ou multiomiques
    • Assurer un rôle d'expert en statistique au sein de la communauté scientifique (BIOASTER et ses partenaires) : communication, formation, support aux projets de recherche, rédaction de rapports /articles scientifiques
    • Participer pleinement aux projets collaboratifs en développant des interactions avec les partenaires projets chaque fois que nécessaire.

    REQUIREMENTS

    Compétences :
    • Niveau ≥ Bac+5, titulaire d'un doctorat, d'un diplôme d'ingénieur ou d'un Master en statistique, biostatistique ou mathématiques appliqués, avec 2-3 ans d'expérience dans une fonction similaire en milieu industriel ou PME ou académique
    • Excellentes connaissances théoriques et appliquées dans les domaines de l'analyse et le traitement de données, de la modélisation statistique, de la méta-analyse, de l'apprentissage automatique et du data mining
    • Maitrise des langages usuels de programmation (R, Python)
    • Connaissances en génomique/transcriptomique sont fortement appréciées
    • Capacité à communiquer avec d'autres disciplines,
    • Excellent niveau d'anglais.
    Aptitudes personnelles :
    • Motivation et engagement
    • Intérêt pour la veille et les développements technologiques
    • Rigueur, organisation et méthode
    • Capacité de travailler de façon autonome et en interaction avec différents types d'interlocuteurs
    • Aisance relationnelle
    • Capacités d'analyse et esprit de synthèse
    • Capacités d'argumentation et de conviction

    LOCALE

    Lyon, France

    HOW TO APPLY

    Please visit www.bioaster.org/en/j[...]02056

    DEADLINE

    October 2018

     

    Copyright © 2018 · Scilico, LLC