석박사 학위 지원자 가이드

 *모든 지원자는 KAIST 및 바이오및뇌공학과 입학 기준 (영어, 학점 등)을 통과할 수 있어야 함.

 

1. 연구실 지원 서류 (application documents for lab)

   - 석사 지원자는 입시 후, 박사 지원자는 입시 전 연구실에 지원함.

     (MS applicants apply after KAIST admission, PhD applicants apply before KAIST admission.

1) 자기 소개서 (GPA, Courses taken, Research experiences, Research outputs)  

   - 학력, 경력, 이수 과목 (학점), 직접 수행해본 프로젝트, 개발, 실험 등 본 연구실과 관련된 학습 및 연구능력 설명

   - 저널 게재, 특허 출원/등록 사항 및 주저자 여부 등 본인 기여도 (해당자)

2) 연구(학업) 계획서 (Research Plan specifically in SynBi lab based on previous study and research experiences)

   - 본 연구실 연구 내용 중 희망하는 연구 목표

   - 학습 및 연구 추진계획 (전략 및 방법 - 본인의 기존 경력을 반영)

   - 석사 또는 박사 후 희망 진로

 

3. Prerequisites 

1) Common prerequisites for all MSc and Ph.D. applicants

   Course: Prob & Statistics, Bioinformatics, Cell Biology and Biochemistry 

   IT Skill Set: R programming, DB SQL programming, Linux shell scripting, Any of Python, Java, or C++ programming 

   Research: Bioinformatics or Omics analysis (term project or individual study level) 

 

2) PhD applicants pursuing bioinformatics engineer in SynBi lab

   *will develop informatics or computational methods to predict diseaes markers or drug candidates 

   Course: Common prerequisites + Linear Algebra, Machine Learning (alternatively AI or Deep Learning), Database, and

              Differential Eq. (alternatively vector calculus or multivariate calculus), Algorithm (alternatively Discrete Math and Data Structure),    

   IT Skill Set: Algorithm implementation, the construction of Oracle DB and its web interface, Linux system management

   Research: Development or analysis in machine learning, data mining, artificial intelligence, or equivalent methods in CS, EE, or Math fields

                     (whole thesis level or Journal/Conference paper level. Subject does not need to be BI) 

 

3) PhD applicants pursuing multi-displinary biologist in SynBi lab

   *will obtain novel disease markers of drug candidates by existing informatics methods and prove them by experimental validations.  

   Course: Common prerequisites + Organic Chemistry, Advanced biology, Pharmacology or Chemistry courses.

   BT Skill Set: Cell biology or Biochemistry experiments with statistical analysis of experimental data.

   Research: Molecular analysis of gene function or drug responses in a cell model or animal/human tissue.

                     (whole thesis level or Journal/Conference paper level)

 

4. 연락처: 이관수 교수 gwansuyi@kaist.ac.kr

 

 

[Additional Helpful Background]

A. Bioinformatics Engineer of SynBi lab: any of

  - Course: Systems Biology (Systems Pharmacology), Pharmaceutical Chemistry, Computational Chemistry (Molecular sturcture modeling), 

               Network analysis, Applied Mathematics (Numerical Analysis, Mathematical Statistics, Stochastic Process, Signals & Systems)

  - IT Skill Set: Parallel computing, Big-data computing

  - Research: Specific research on artificial intelligence applications

 

B. Multi-displinary Biologist of SynBi lab: any of

  - Course: Functional Genomics (Proteomics), Pharmaceutical Chemistry, Molecular Genetics, Immunology, Pharmacology

  - BT Skill Set: Eukaryotic cell culture with molecular or functional assay in a cell model, Genetic or protein engineering.

                    (Molecular probe construction/detection, Confocal/HCS/multiplex experiment)

  - Research: Gene function or drug response study with systems biologiy approach (omics experiment & analysis)