哈佛大学医学院博士后申请要求

  哈佛大学医学院遗传学系招收运算生物学或生物信息学研究博士后。要求生物信息学、运算生物学、计算机科学等相关学科博士学历,熟悉C/C++, Python, Perl, R, HTML, Java等计算机编程语言,擅长基因组学、数据处理、转录或蛋白质组学数据分析等研究。要求申请者的科研成果丰富,已在同行评审的重要期刊上发表学术文章。具备英语口语及书面语的交流能力。以上就是关于哈佛大学医学院博士后的申请要求,更多国外名校博士后/访问学者职位空缺信息厚谱教育会定期更新,欢迎关注!

  Computational Biology/Bioinformatics Post-doctoral position available immediately

  HMS-Genetics-Sinclair

  We are seeking a talented, highly motivated individual to join the laboratory headed by Professor David Sinclair in the Genetics Department at Harvard Medical School, Boston. The Sinclair lab is known for their work on genes and small molecules that delay aging and treat age-related diseases. The lab has a wide range or expertise and interests, including cancer, neurodegeneration, diabetes, and fertility. The laboratory has a record of innovation with over 40 patents and numerous start-up companies. In 2014, Dr. Sinclair was on the TIME100 list of the “most influential people in the world.” This is also an opportunity to innovate and be involved in the foundation of one or more new companies.

  We are seeking a talented individual with a background in computational science or bioinformatics. The position offers an opportunity to work with an engaged group of scientists and participate in cutting edge research combining proteomics and genomics. The ideal candidate will have experience working on Linux-type servers and with programming languages (e.g. C/C++, Python, Perl, R, HTML, Java etc.). The position requires expertise in genomics, transcriptomics and/or proteomics data analysis. The candidate will work on integrative and translational analysis of multiple levels of omics data. The focus will be on applying systems-omics methods to biological research questions and to analyze the acquired data. This includes both the use of established methods of data analysis and identification and testing of new strategies and solutions for the analysis of quantitative biological data. The individual will also participate in the development of our proteo-genomics analysis pipeline.

  Principal Responsibilities:

  Lead in the development of new methods and algorithms/pipelines to analyze next-generation sequence of genomes, epigenomes, and transcriptomes derived from experimental specimens.

  Lead in the development of computational tools and resources/databases to integrate genomics and functional genomics datasets.

  Identification of regulatory networks through integration of multiple layers of data, including but not limited to genome sequences, gene and small RNA expression profiles, metabolite profiles, and DNA methylation profile.

  Implement new analytical tools capable of troubleshooting and accelerating the data analysis.

  Work with the molecular biology team to develop protocols that enable parallel interpretation of sequencing and proteomics data.

  Process, analyze and interpret high volumes of data as part of wide range of internal and external scientific studies. Apply automated methods and software to support large-scale bioinformatics analysis.

  Provide bioinformatics resources for day-to-day use by members of the laboratory.

  Monitor and evaluate analytical aspects of new and emerging technologies (sequencing proteomics and others).

  Present scientific and technical data to both internal and external scientific colleagues in a clear and cohesive manner.

  Work independently and prepare timetables, deliverables, and project schedules.

  Required Skills/Abilities/Competencies:

  Ph.D. in Bioinformatics, Computational Biology, Computer Science or related fields.

  Experience with and deep understanding of algorithms, data structures, and scientific programming for analysis of biological data.

  Proven understanding and experience in the fields of genomics, data processing, high-throughput data analysis, and genomic databases

  Must have experience with sequence data formats and processing from Next Generation platforms (Illumina, PacBio, etc.)

  Software engineering skills with proficiency in Java, C++ Perl, Python and R

  Excellent track record of analyzing next generation sequencing data, particularly WGS, HiC-seq, ChIP-seq, and RNA-seq.

  Experience in a Unix/Linux environment.

  Experience with common tools for

  NGS (BWA, GATK, STAR, Samtools, Picard, TopHat, Cufflinks, etc.)

  ChiP-seq; HiC-seq (Bowtie, MACS, SICER, HOMER, etc.)

  Proteomics (mzR, mzLD, etc.)

  Metabolomics (metaboAnalyst, etc.)

  De novo assembly (e.g. SOAPdenovo, Velvet, Abyss, and ALLPATHS-LG)

  Genome annotation (e.g. Augustus, MAKER)

  Experience in algorithm development for analysis of massively parallel sequencing data (NGS outputs of ChiP-seq, ATAC-seq, bulk cell and single cell RNA-seq, and whole genome sequencing).

  Experience with statistical analysis is strongly preferred.

  Strong scientific understanding of molecular and cellular biology, genetics and genomics.

  Candidates must demonstrate outstanding personal initiative.

  Excellent teamwork, time management and organizational skills.

  Ability to work independently in a multidisciplinary, fast-paced, dynamic and results-oriented environment.

  Ability to present data to a multidisciplinary audience in a clear and cohesive manner.

  Ability to meet deadlines and multitask efficiently is a must.

  The ideal candidate will have a record of scientific rigor and creativity, a strong publication record in peer-reviewed scientific journals and the ability to work in teams. Candidates should have a strong background in genomics, proteomics and metabolomics. Excellent oral and written communication skills are required. The individual will present regular updates to academic and industry collaborators as well as prepare and publish research reports. There is a real opportunity to invent and to innovate.

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