一、院校简介

奥斯陆大学（挪威文：Universitetet i Oslo）是挪威王国最高学府，位于首都奥斯陆，由丹麦-挪威联盟时期末代国王弗雷德里克六世于1811年下令建立。为了纪念该位国王，大学最初被命名为皇家弗雷德里克大学（挪威文：Det Kongelige Frederiks Universitet），1939年更名为奥斯陆大学。奥斯陆大学目前拥有将近40000名在校学生、5000名教职员工、3000名国际留学生，是挪威规模最大的高等院校。奥斯陆大学目前下辖8个学院、69个系，还坐拥众多的植物园、博物馆、珍藏馆、陈列室、天文台、研究院、实验室、图书馆、附属医院等等机构。

奥斯陆大学位列2024QS世界大学排名第117位。

二、招聘职位

挪威分子医学中心(NCMM)，奥斯陆大学医学院，北欧EMBL合伙人Marieke Kuijjer领导的计算生物学和系统医学组提供一个为期3年的博士后职位。该职位是挪威癌症协会Krafttak mot克雷夫特运动最近资助的项目“转移性乳腺癌分层治疗的精确医学”的一部分。理想的开始日期是在2024年冬/春，但我们是灵活的，这可以讨论。

该小组成立于2018年，专注于使用计算方法来理解驱动癌症发展、进展和异质性的分子机制。该小组的驱动假设是，我们在癌症中观察到的复杂临床表型无法通过分子数据的个体层来充分定义。相反，我们必须考虑能够驱动癌症表型的不同生物成分之间的潜在相互作用网络。为此，该小组开发了将基因组数据置于大规模全基因组调控网络环境中的计算方法，以及针对个体癌症患者分析此类网络的方法。

其中，我们目前的项目旨在开发新的计算方法和工具，用于

(1)增强子-启动子相互作用的建模；

(2)使用深度学习整合多模态单细胞数据；

(3)全基因组调控网络的微调分析；

(4)患者特异性调控网络与多组数据的整合；

(5)基于单细胞和空间转录数据的网络建模。

该项目旨在开发和使用大规模全基因组调控网络建模和分析方法，以获得乳腺癌转移中所涉及的调控破坏的详细解决方案。被选中的候选人的角色将是为个体转移性乳腺癌样本及其原发性肿瘤建立基因调控网络模型，包括来自公共资源和通过该项目产生的内部数据的整体和单细胞数据。候选人将应用配对网络分析方法来揭示乳腺癌转移特异性调控程序和转移肿瘤细胞中的潜在亚型。候选人将与小组中的其他研究人员密切合作，实施新开发的网络分析方法。也将有机会开发基因组网络分析的新方法。

A 3-year postdoctoral position is available in the Computational Biology and Systems Medicine group led by Marieke Kuijjer at the Centre for Molecular Medicine Norway (NCMM), Faculty of Medicine, University of Oslo, Nordic EMBL partner . The position is part of the recently funded project "Precision medicine for treatment stratification of metastatic breast cancer" from the Krafttak mot Kreft campaign, Norwegian Cancer Society. The ideal start date is in winter/spring 2024, but we are flexible and this can be discussed.

The group was established in 2018 and focuses on using computational approaches to understand the molecular mechanisms that drive cancer development, progression, and heterogeneity. The group's driving hypothesis is that the complex clinical phenotypes we observe in cancer cannot be adequately defined by individual layers of molecular data. Instead, we must consider the underlying network of interactions between the different biological components that can drive cancer phenotypes. To do so, the group develops computational approaches that place genomic data into the context of large-scale, genome-wide regulatory networks, as well as approaches to analyze such networks for individual cancer patients.

Amongst others, our current projects aim at developing new computational methods and tools for 

(1) modeling enhancer-promoter interactions,

(2) integration of multi-modal single cell data using deep learning, 

(3) fine-tuned analysis of genome-wide regulatory networks, 

(4) integration of patient-specific regulatory networks with multi-omic data, 

(5) modeling networks based on single cell and spatial transcriptomic data. 

The project aims to develop and use large-scale, genome-wide regulatory network modeling and analyses approaches to obtain a detailed resolution of the regulatory disruptions involved in breast cancer metastasis. The selected candidate's role will be to model gene regulatory networks for individual metastatic breast cancer samples and their primary tumors-of-origin, both on bulk and single-cell data, from publicly available resources as well as in-house data generated through the project. The candidate will apply paired network analysis approaches to uncover breast cancer metastasis-specific regulatory programs and potential subtypes in metastatic tumor cells. The candidate will closely work with other researchers in the group to implement newly developed network analysis methods. There will also be an opportunity to develop new approaches for genomic network analysis.

三、职位要求

我们寻求一位有高通量基因组数据计算分析记录的高度积极的候选人。候选人应该对应用计算工具回答系统医学中的问题感到兴奋，重点是乳腺癌。理想的候选人应该具有合作精神和创造力，拥有强大的致力于大规模基因组数据分析的编程技能，并有良好的出版记录。该职位要求具备分析大规模基因组数据集的经验。最好有基因组数据整合、生物网络分析和/或基因调控方面的经验，但不是这个职位的必要条件。有乳腺癌研究背景是一个优势。该职位面向拥有计算生物学、生物信息学、生物统计学、癌症基因组学、网络科学或相关领域博士学位的申请人开放。

这是一个全职职位，任期三年，根据未来的资金情况可能会延长。

资格要求

-计算生物学、生物信息学、生物统计学、癌症基因组学、网络科学或相关领域的博士学位。欢迎具有生物学或(生物)医学博士背景的申请人申请，但应具备记录在案的编程技能。

-优秀的出版记录(包括第一作者)。

-精通编程(如R、Python、MATLAB、Bash)。

-高通量基因组数据分析经验。

-了解基因调控、(乳腺癌)癌症和/或网络生物学被视为优势。

-具有高性能计算经验者优先。

-英语专业水平。

-愿意在团队环境中工作，分享技能和想法，并在项目上合作。

We seek a highly motivated candidate with a track record of computational analysis of high-throughput genomics data. The candidate should be excited about applying computational tools to answer questions in systems medicine, with a focus on breast cancer. The ideal candidate is collaborative and creative, has strong programming skills dedicated to the analysis of large-scale genomics data, and has a strong publication record. Experience with analysis of large-scale genomic data sets is a requirement for this position. Experience with genomic data integration, biological network analysis, and/or gene regulation is desirable, but not required for this position. A background in (breast) cancer research is an advantage. The position is open to applicants with a PhD in computational biology, bioinformatics, biostatistics, cancer genomics, network science, or related fields.

The appointment is a fulltime position and is made for a period of three years with possible extension depending on future funding.

Qualification requirements

-PhD degree in computational biology, bioinformatics, biostatistics, cancer genomics, network science, or a related field. Applicants with a PhD background in biology or (bio)medicine are welcome to apply but should have documented programming skills.

-Strong publication record (including 1st authorship).

-Proficiency in programming (such as R, Python, MATLAB, Bash).

-Experience with analysis of high-throughput genomics data.

-Knowledge in gene regulation, (breast) cancer, and/or network biology is considered an advantage.

-Experience with high performance computing is desirable.

-Professional proficiency in English.

-Willingness to work in a  team environment, sharing skills and ideas, and collaborating on projects.

四、申请方式

申请必须包括:

(1)一封求职信，说明你的动机、科学背景和研究兴趣。

(2)一份详细的简历，包括出版物清单。

(3)2-3份参考资料(姓名、机构、电子邮件、电话号码和与候选人的关系)。

这些应该以pdf格式上传。请通过公告中链接的网络招聘系统提交您的申请。虽然招聘系统包括一份基本的简历，但我们要求候选人还包括一份单独的、详细的简历。pdf格式及其应用。没有求职信和/或详细简历的申请将被拒绝。

在评估申请时，将特别强调有文件证明的学术资格、项目描述(在招聘申请人时有此要求)、项目质量以及候选人的动机和个人适合性。将安排对最合格候选人的面试。

预计成功的候选人将能够在就业期间完成该项目。

The application must include:

(1) A cover letter, stating your motivation, scientific background, and research interests.

(2) A detailed CV with a list of publications.

(3) 2-3 references (name, institution, e-mail, telephone number, and relation to the candidate).

These should be uploaded in pdf format. Please submit your application through the web-based recruitment system linked in the announcement. While the recruitment system includes a basic CV, we ask candidates to also include a separate, detailed CV in .pdf format with their application. Applications without a cover letter and/or detailed CV will be rejected.

In assessing the applications, special emphasis will be placed on the documented, academic qualifications, the project description (whenever this is required in the call for applicants), and the quality of the project as well as the candidates motivation and personal suitability. Interviews with the best qualified candidates will be arranged.
It is expected that the successful candidate will be able to complete the project in the course of the period of employment.

联系方式

info@zhengzicn.com