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Research Plan/Proposal/Interest写作要点及模板

来源:未知 作者:无城 人气: 发布时间:2010-11-06
摘要:有时候,国外雇主会要求非英语语言国家的学生书写一份research plan,以此来考察对方的语言功底,以及研究兴趣、思路是否和自己的实验室是否很match。当然也不乏个别卑劣之徒,假招聘之名,索取research plan,以寻觅idea。 简介 Research Plan/Proposal就是
有时候,国外雇主会要求非英语语言国家的学生书写一份research plan,以此来考察对方的语言功底,以及研究兴趣、思路是否和自己的实验室是否很match。当然也不乏个别卑劣之徒,假招聘之名,索取research plan,以寻觅idea。

简介
Research Plan/Proposal就是我们通常说的研究计划,申请者一般简称为RP。与PS不同,研究计划(Research Proposal)是纯学术的,无须加入诸如个人经历、感情这些东西。它要求申请者要有一个明确的研究方案并对此有较好的掌握。这个计划必须是非常具体的,不能太宽泛。研究计划一般包括以下几点:
1)        研究对象(Topic),即你想要解决的问题
2)        研究此问题的意义
3)        前人的研究状况即遗留的问题
4)        你的研究方法(Methodology)
5)        参考文献(Bibliography)
具体的要求每个大学都略有不同,但基本上是依照这个格式。首先,你要根据你想申请的或者你比较熟悉的研究方向来提出一个设想,然后再把相关的背景,你设想的研究方法,以及你所预期的得到的结果都写进去。这就类似于研究生的开题报告。

框架
通常你要申请的学校会提供给你一个RP的写作大纲,有时也会规定写作的长度,一般要在3到5页左右。当然,除非学校规定,你不一定要按照对方学校给你的格式来写。
下面是香港大学(HKU)为工学院的申请人提供的RP模板,供大家参考:
Applicant’s name:
Proposed Topic/Title of Research:
Background:
(Please provide background material that explains the motivation for and aims and objectives of your proposed study, outline the theoretical framework that forms the basis of your research, and specify the research question(s) you intend to investigate.)
Methodology:
(Please give details of subjects, data collection procedures, proposed data analysis and the statistical approach you intend to adopt, if applicable.)
Outcomes and Value:
(Please indicate the expected outcomes of your research and the significance to the various fields of application or contexts, the anticipated impacts or value of the results and their interpretation.)

写作要点
RP写作的第一步,就是Topic的确定。也就是你的所要研究的具体内容。主题的确定一方面可以多看一些本专业的综述,找到一些有类似项目,但是还没有人做过的东西;也可以找到自己专业的导师师兄师姐来帮你;当然,如果你自己有新的想法提出那就更好不过了。其实写文章题目越小,范围越明确,就越好写,讲清楚就行,论证方法才是写作中的重点。
在确定主题之后,下面需要提供一些你的研究工作的背景情况,也就是Background。比如你要研究生物新能源,那么你就要描述一下现在世界上的能源状况,都开发利用过那些生物资源。一方面让人了解一下现状,另一方面也告诉别人,你为什么要来研究这个东西。
Methodology是你设计的研究方法,也就是用何种方法来实现你的设想,达到你所预期的结果。包括有实验步骤,所需时间,以及实验结果的分析方法等。比如生物新能源,你就要写出你是利用何种生物,以什么方法来提取这种资源,还有最后产能的效率要如何测量。这部分的写作要具体,Step by Step的告诉人家你是如何来做这个实验的。Methodology可以最直接的反应一个人的动手能力。
接下来就是你预期的实验结果了,实验结果反应出你设计的研究到底有没有意义。还是生物能源的例子,你提出这样一个设想,那么你想看到的结果一定是,这种新的能源一方面产能效率高,另一方便又经济环保,成本低廉,因此开发新能源非常的有前景。
文章的最后要把你从确定题目到查阅背景到制定研究方案所参考过的所有资料都列出来。

总结
要注意的是,这个研究计划是展望性的,而不是你正在做,或者已经完成的东西。Research Proposal里面的内容并不一定真的能够实现,而只是你基于现有知识提出的一个研究方案,它的作用就是让对方学校看看申请者是否具备作为研究者应有的创新能力以及解决问题的能力。
1.实验项目背景, 
2.对方实验室 已有的研究
3.进一步想做的事情
3.1 实验大致方向
3.2 实验大致技术
3.3实验预期
4.实验进程.
签名
很多大学的网址上有各位教授的cv,也许可以参考一下.

模板一. Research Interest Statement
My research interests concentrate on applying statistical data mining and machine learning techniques to system biology. I am especially interested in developing and applying statistical learning algorithms to identify patterns from large amounts of high dimensional data that reflect the states of the signal transduction system. As a pharmacologist, I am always intrigued by cellular signal transduction pathways and complexity of the system. Before my transition to the computational biology field two years ago, my research as a pharmacologist had mainly concentrated on individual pathways or protein molecules. It often occurred to me that the biomedical research of the last few decades had accumulated a wealth of knowledge at the molecular level, and it is time for one to take a step back and view the cellular signal transduction system as a full-fledged forest with most of the leaves painted colorfully. Advance in biological techniques, such as DNA microarray and high through-put screening, has produced large amounts of data regarding many aspects of cell. These data offer biologists opportunities to study the cellular system, but also pose challenges for conventional biologists. The transition from an experimental to computational biologist was quite natural for me because of my long-lasting interest and experience in scientific computing. Winning the National Library of Medicine training grant award provided me a great opportunity to extend my research ability in this direction. My study and research benefited greatly from the exceptionally excellent artificial intelligence and statistics community in Pittsburgh area.
My current research in computational biology falls in two major areas, which are described below:
The first is to develop a latent variable generative model, variational Bayesian cooperative vector quatizer (VBCVQ) model, to analyze the DNA microarray data and model the gene transcription regulation pathways. I have finished mathematical derivation and implementation of the model. In addition to its potential biological application, the model can be used in a wide range of applications, e.g. image processing, image compression and content-based image retrieval. The model closely simulates the gene expression regulation system. It can overcome some drawbacks of the commonly used existing techniques and address questions other models fail to address. Generally, the model has following advantages: (1) Data dimension reduction. (2) Identification of the key components of gene expression regulation pathways. (3) Capability of inferring the state of key components when given new microarray data. Such information can be useful for further exploring the mechanism of disease, drug effect or toxicity and the construction of diagnosis tools. Full Bayesian learning of the model allows us to address questions like ``what is the most efficient way to encode the information controlling gene transcription?'' or ``what are the key signal transduction components that control gene expression in a given kind of cell?'' Currently, I am testing the model with image encoding and mixed image separation. Once this stage finished, I will apply the model in microarray analysis.
The second area I am working on is to identify and predict the function of a protein motif using data mining approaches. The Gene Ontology is a set of annotations that describe the biological system in a hierarchical fashion. The current Gene Ontology database can also serve as a knowledge base to facilitate biological discovery because it contains a large amount of information regarding the molecular function, biological process and cellular location of proteins. To make effective use of such a knowledge base, a biologist would like to query the knowledge base in the following fashion: ``what is the protein motif that encodes a given molecular function?'' or ``what is the potential function of a conserved motif we identified?'' However, the current Gene Ontology database can not answer such queries due to the way of information being stored and the potential ambiguity caused by a conventional database query, even though the information is actually available. Working with collaborators at the University of Pittsburgh and Carnegie Mellon University, I have developed a general method to address the issue using data mining approaches. We have extracted a set of features that help to disambiguate the association of protein motifs and the Gene Ontology terms. Then, we trained a statistical classifier to determine whether a Gene Ontology term should be assigned to a protein motif, using probability to reflect the confidence or uncertainty. The method performs well when tested on known protein motifs from PROSITE. I will further extend the work in two directions: (1) To develop a system based on the method and make it available to the scientific community for data mining. (2) To study the evolution of protein sequence motifs by further exploiting the knowledge in Gene Ontology with hierarchical aspect models. These studies will help identify the key residues among the motifs, and allow us to address the questions like ``what amino acid plays the key role in proteins that act as kinase or reductase/oxidase?''
Overall, my training in both experimental and computational biology enables me to combine the knowledge of both fields without any communication gap. I foresee that my research will follow both directions of computational method development and biological discovery. As a computational biologist, I will extensively collaborate with both experimental biologists and computer scientists to solve interesting biological problems. My short term goal is to further extend my current research as described above. In the long run, I will continue to learn, identify, develop and apply computational methods in the fields of drug discovery, drug toxicity prediction and developing diagnostic tools based on biological data.

模板二-Statement of research interests and future research plans
1 Current research
作者:无城

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