瑞典隆德大学2021年招聘博士后职位（生物信息学）

Bioinformatician At LUDC

Lund University

Description

Job assignments

Applicants are invited to apply for a position as bioinformatician at LUDC. The employment is a fixed-term employment for two years (100 %) with preliminary start date 1 October 2021.

The successful applicant will be part of the Bioinformatics Unit at the LUDC including bioinformaticians, a biostatistician, a data manager, a computer engineer and an IT-data manager (https: // www. ludc.lu.se/resources/ludc- bioinformatics-unit). The Unit provides bioinformatics, biostatistics and computational support to anyone affiliated with LUDC, as well as access to a high-performance computing environment. The support includes involvement in long-term research projects, analyses in short-term projects, consultation, and training.

To further the mission of the LUDC, we are looking for an enthusiastic and talented person to strengthen the bioinformatics expertise of our Unit. A successful candidate will be involved in all aspects of computational/bioinformatics pipeline development, implementation and management, to support analyses of various data including but not limited to genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics and clinical data.

Another important part of the work that the candidate will perform includes quality control, processing, and analysis of the data for the Human Tissue Laboratory (HTL), a key resource for LUDC researchers. The HTL collects human tissue samples for research purposes and performs central analyses on these materials, available to colleagues at the LUDC and Uppsala University.

We expect the successful applicant to be an experienced bioinformatician who can implement custom pipelines for various omics analyses. The applicant is also expected to have excellent communication skills, a willingness to interact with other researchers, interpret requests and deliver bioinformatics solutions to non-bioinformaticians. Also, the applicant is expected to participate in the training of LUDC staff and scientists in the selection and use of bioinformatics tools.

The bioinformatics field is broad and developing at an impressive pace. Thus, the candidate is expected to independently follow the development of the field, work on improving existing pipelines and present alternative solutions on how to best utilize available data and resources.

Qualifications

PhD in Bioinformatics, Mathematics, Molecular Biology, Statistics, Computer Science or related field, with a proven track record in applying quantitative methods to data analysis tasks, as well as programming and computing cluster experience.

Knowledge of next generation sequencing pipelines and experience manipulating, analyzing, and annotating very large biological data sets (e.g. RNA-seq, SNPs arrays, WGS, ChIP-seq, ATAC-seq), both in exploratory and pipelined fashions.

Experience in data analysis and data mining to identify trends and produce meaningful data visualizations.

Experience in using common bioinformatics tools and statistical methods (e.g. DESeq, edgeR, GATK, PLINK, STAR, BWA, Salmon, Kallisto, FastQC).

Proficiency in R and knowledge and experience of at least one more programming language (e.g. Python, Perl, Java).

Experience with web-based bioinformatics tools and public domain biological databases.

Highly capable of being involved collaboratively with an interdisciplinary team, including laboratory scientists, medical doctors, epidemiologists, bioinformaticians, statisticians, and data analysts.

We will also pay attention to personal attributes, favoring individuals that are self-motivated, flexible and have the ability to work independently in a well-documented way and to prioritize and comply with deadlines.

Documented oral and written proficiency in English.

Other preferred skills

Experience working with reproducible research tools (e.g. RMarkdown, Git, Conda, Snakemake).

GitHub profile with code examples is advantageous.

Experience with multi-omics data integration.

Experience with metagenomic and single-cell data analyses.

Knowledge of machine learning algorithms and frameworks, such as scikit- learn, Keras, Tensorflow, although not required, will be seen as an asset.

Outstanding communication skills (verbal and written) including public speaking and poster presentation.

Experience of working in an international environment.

Complete application should include

Cover letter

CV

List of publications

Contact information of 3 references.

We anticipate a need to cover the described job tasks beyond the employment period.

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