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  Perry Moerland
Silvia Olabarriaga
Barbera van Schaik
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Aldo Jongejan
 

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AMC Graduate School
Bioinformatics Sequence Analysis DNA Technology Proteomics, Mass Spectrometry and Protein Research
Computing in R e-Science (pilot) Introduction to Unix
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Introduction to Bioinformatics Bioinformatics
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Introduction to Bioinformatics Bioinformatics
 
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Pattern Recognition (BioSB Research School) Optimization (NBIC PhD School) Python
 
NBIC Tutorial NBIC RNA-seq Bioinformatics/e-Bioscience Show Cases
Molecular Profiling (RU, Chemical Biology)    
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AMC Graduate School
Bioinformatics Sequence Analysis DNA Technology Proteomics, Mass Spectrometry and Protein Research
Computing in R e-Science (pilot) Introduction to Unix
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Introduction to Bioinformatics  
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Introduction to Bioinformatics Bioinformatics
 
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The Bioinformatics Laboratory offers students the possibility to do a bioinformatics and e-science projects ('stage') to further specialize in these scientific fields. The minimum period for a practical period is three months, but it depends on the background and skills of the student. The student is assumed to have programming and other skills (e.g., statistics, relational databases, bioinformatics, distributed computing, web programming) to successfully engage in a project. During the practical period the student is allowed to participate in bioinformatics courses or meetings that are organized by the Bioinformatics Laboratory.

If you are interested then contact Antoine van Kampen (a.h.vankampen@amc.uva.nl) to discuss interests and possibilities.
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Trainee projects

  • Bioinformatics analysis of LC-MS metabolomics data
    • Supervisor: Antoine van Kampen, a.h.vankampen@amc.uva.nl, 020-5667096
    • Description: Liquid chromatography coupled to mass spectrometry (LC/MS) is used in metabolomics research. In this context, the technology has been increasingly used for e.g, the discovery of biomarkers. One of the challenges in this domain remains development of better approaches for the bioinformatics analysis of LC/MS data. In this project we aim to develop a processing pipeline for pre-processing and statistical analysis of metabolomics data.
    • Technical skills: The students should have programming skills and interest in bioinformatics. Knowledge and interest in statistical analysis is important. The methods will be developed in the R-statistical package and we will explore the use of Taverna for workflow management.

  • An in-silico approach for the detection of contaminated tumor cell lines
    • Supervisor: Perry Moerland, p.d.moerland@amc.uva.nl, 020-5664660
    • Description: For decades, hundreds of different human tumor type-specific cell lines have been used in experimental cancer research as models for their respective tumors. However, sometimes cell lines that have been used for years turn out to be contaminated (see here for a recent example). In this project you will work on an approach that uses publicly available microarray datasets of human cancer cell lines, primary tumors, and tissues to detect contaminated cell lines. The approach is based on the intuitive idea that often contamination can be traced back to different tumor types or tissues. We expect that these signatures of contamination can also be found in the cell line's gene expression profile. Our lab already has most tools in place to integrate multiple microarray datasets in a single database. Focus of this project would be on developing the methods for comparing cell line gene expression data with other datasets. The ultimate goal would be a tool to which a researcher can submit his cell line gene expression data and that scores the likelihood of the cell line being contaminated.
    • Technical skills: knowledge of bionformatics in general and specifically the statistical programming language R would be an advantage. Following our MSc level course at the University of Amsterdam on the analysis of genome-wide experiments provides most of the required skills.

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MIK1.2 Biomedische basis principes MIK2.1 Databases en computernetwerken MIK3 DGO-Bioinformatics
MIK Master, Current issues in medical informatics I MIK GRID Summer school: Bridging Health Care with IT
MIK Bachelor Seminars 2011 MIK Bachelor Seminars 2013
MIK Bachelor Databases and Networks 2013
 
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Biomedical Sciences/Biology (SILS; BW02K) Medical Molecular Systems Biology (SILS 051BMS) Communicatie in de biologie (1003B)
Miniscriptie (SILS 290BMW) Genomics of Disease Medical Biotechnology
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UvA Faculty of Science: Informatics
 
Bioinformatics-I (Computational Sciences) Bioinformatics-II Microarrays (Computational Sciences) Bioinformatics-II NGS (Computational Sciences)
Amsterdam Graduate School of Sciences (AGSS)
Translational Medicine    
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Computing in R e-Science (pilot) Introduction to Unix
Introduction to Bioinformatics  
Other
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NBICTutorial NBIC RNA-seq Bioinformatics/e-Bioscience Show Cases
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NBIC Tutorial NBIC RNA-seq Bioinformatics/e-Bioscience Show Cases
 
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  If you are interested then contact Antoine van Kampen (a.h.vankampen@amc.uva.nl) to discuss interests and possibilities.
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  • An in-silico approach for the detection of contaminated tumor cell lines
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    • Description: For decades, hundreds of different human tumor typeďż˝specific cell lines have been used in experimental cancer research as models
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    • Description: For decades, hundreds of different human tumor type-specific cell lines have been used in experimental cancer research as models
  for their respective tumors. However, sometimes cell lines that have been used for years turn out to be contaminated (see here for a recent example). In this project you will work on an approach that uses publicly available microarray datasets of human cancer cell lines, primary tumors, and tissues to detect contaminated cell lines. The approach is based on the intuitive idea that often contamination can be traced back to different tumor types or tissues. We expect that these signatures of contamination can also be found in the cell line's gene expression profile. Our lab already has most tools in place to integrate multiple microarray datasets in a single database. Focus of this project would be on developing the methods for comparing cell line gene expression data with other datasets. The ultimate goal would be a tool to which a researcher can submit his cell line gene expression data and that scores the likelihood of the cell line being contaminated.
    • Technical skills: knowledge of bionformatics in general and specifically the statistical programming language R would be an advantage. Following our MSc level course at the University of Amsterdam on the analysis of genome-wide experiments provides most of the required skills.

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NBICTutorial NBIC RNA-seq Bioinformatics/e-Bioscience Show Cases
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  • An in-silico approach for the detection of contaminated tumor cell lines
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    • Description: For decades, hundreds of different human tumor typeďż˝specific cell lines have been used in experimental cancer research as models
  for their respective tumors. However, sometimes cell lines that have been used for years turn out to be contaminated (see here for a recent example). In this project you will work on an approach that uses publicly available microarray datasets of human cancer cell lines, primary tumors, and tissues to detect contaminated cell lines. The approach is based on the intuitive idea that often contamination can be traced back to different tumor types or tissues. We expect that these signatures of contamination can also be found in the cell line's gene expression profile. Our lab already has most tools in place to integrate multiple microarray datasets in a single database. Focus of this project would be on developing the methods for comparing cell line gene expression data with other datasets. The ultimate goal would be a tool to which a researcher can submit his cell line gene expression data and that scores the likelihood of the cell line being contaminated.
    • Technical skills: knowledge of bionformatics in general and specifically the statistical programming language R would be an advantage. Following our MSc level course at the University of Amsterdam on the analysis of genome-wide experiments provides most of the required skills.

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Translational Medicine    
AMC Graduate School
Bioinformatics Sequence Analysis DNA Technology Proteomics, Mass Spectrometry and Protein Research
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Computing in R e-Science (pilot) Introduction to Unix
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Medical Informatics (AMC)
MIK1.2 Biomedische basis principes MIK2.1 Databases en computernetwerken MIK3 DGO-Bioinformatics
MIK Master, Current issues in medical informatics I MIK GRID Summer school: Bridging Health Care with IT
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Biomedical Sciences/Biology (SILS; BW02K) Medical Molecular Systems Biology (SILS 051BMS) Communicatie in de biologie (1003B)
Miniscriptie (SILS 290BMW) Genomics of Disease Medical Biotechnology
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Amsterdam Graduate School of Sciences (AGSS)
Translational Medicine    
AMC Graduate School
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Introduction to Bioinformatics DNA Technology Proteomics, Mass Spectrometry and Protein Research
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Bioinformatics Sequence Analysis DNA Technology Proteomics, Mass Spectrometry and Protein Research
 
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Introduction to Unix Pattern Recognition (NBIC PhD School) Optimization (NBIC PhD School)
NBICTutorial NBIC RNA-seq Bioinformatics/e-Bioscience Show Cases
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Introduction to Bioinformatics 2000-2013  
 
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Translational Medicine    
AMC Graduate School
Introduction to Bioinformatics DNA Technology Proteomics, Mass Spectrometry and Protein Research
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Computing in R e-Science  
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Computing in R e-Science (pilot)  
 
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Introduction to Unix Pattern Recognition (NBIC PhD School) Optimization (NBIC PhD School)
NBICTutorial NBIC RNA-seq Bioinformatics/e-Bioscience Show Cases
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Information for trainees
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The Bioinformatics Laboratory offers students the possibility to do a bioinformatics project ('stage') to further specialize in this scientific field. The minimum period for a practical period is three months but depends on the background and skills of the student. The student is assumed to have programming and other skills (e.g., statistics, relational databases, bioinformatics) to successfully engage in a project. During the practical period the student is allowed to participate in bioinformatics courses or meetings that are organized by the Bioinformatics Laboratory.
>
>
The Bioinformatics Laboratory offers students the possibility to do a bioinformatics and e-science projects ('stage') to further specialize in these scientific fields. The minimum period for a practical period is three months, but it depends on the background and skills of the student. The student is assumed to have programming and other skills (e.g., statistics, relational databases, bioinformatics, distributed computing, web programming) to successfully engage in a project. During the practical period the student is allowed to participate in bioinformatics courses or meetings that are organized by the Bioinformatics Laboratory.
 

If you are interested then contact Antoine van Kampen (a.h.vankampen@amc.uva.nl) to discuss interests and possibilities.
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MIK1.2 Biomedische basis principes MIK2.1 Databases en computernetwerken MIK3 DGO-Bioinformatics
MIK Master, Current issues in medical informatics I MIK GRID Summer school: Bridging Health Care with IT
MIK Bachelor Seminars    
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Biomedical Sciences/Biology (SILS; BW02K) Bioinformatics-I (Computational Sciences) Bioinformatics-II (Computational Sciences)
Medical Molecular Systems Biology (SILS 051BMS) Communicatie in de biologie (1003B)  
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UvA Faculty of Science: SILS
Biomedical Sciences/Biology (SILS; BW02K) Medical Molecular Systems Biology (SILS 051BMS) Communicatie in de biologie (1003B)
UvA Faculty of Science: Informatics
Bioinformatics-I (Computational Sciences) Bioinformatics-II Microarrays (Computational Sciences) Bioinformatics-II NGS (Computational Sciences)
 
AMC Graduate School
Introduction to Bioinformatics DNA Technology Proteomics, Mass Spectrometry and Protein Research
Other
Introduction to Unix Pattern Recognition (NBIC PhD School) Optimization (NBIC PhD School)
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NBICTutorial
 
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Antoine van Kampen
Perry Moerland
Silvia Olabarriaga
Barbera van Schaik
Marcel Willemsen
Angela Luyf
Miranda Stobbe
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Antoine van Kampen
Perry Moerland
Silvia Olabarriaga
Barbera van Schaik
Marcel Willemsen
Angela Luyf
Miranda Stobbe
 

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Information for trainees
The Bioinformatics Laboratory offers students the possibility to do a bioinformatics project ('stage') to further specialize in this scientific field. The minimum period for a practical period is three months but depends on the background and skills of the student. The student is assumed to have programming and other skills (e.g., statistics, relational databases, bioinformatics) to successfully engage in a project. During the practical period the student is allowed to participate in bioinformatics courses or meetings that are organized by the Bioinformatics Laboratory.
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  See also Research projects for master students (Biological, Biomedical, and life sciences)
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Trainee projects

  • Bioinformatics analysis of LC-MS metabolomics data
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MIK1.2 Biomedische basis principes MIK2.1 Databases en computernetwerken MIK3 DGO-Bioinformatics
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MIK Master, Current issues in medical informatics I MIK GRID International class of Medical Informatics
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UvA Faculty of Science
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Biomedical Sciences/Biology (SILS; BW02K) Bioinformatics-I (Computational Sciences) Bioinformatics-II (Computational Sciences)
 
AMC Graduate School
Introduction to Bioinformatics DNA Technology Proteomics, Mass Spectrometry and Protein Research
Other
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Bioinformatics-II: Analysis of genome wide expression data, April 6 - May 6, 2010

DNA technology course , March 2010
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Antoine van Kampen
Perry Moerland
Silvia Olabarriaga
Barbera van Schaik
Marcel Willemsen
Angela Luyf
Miranda Stobbe
 
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  for their respective tumors. However, sometimes cell lines that have been used for years turn out to be contaminated (see here for a recent example). In this project you will work on an approach that uses publicly available microarray datasets of human cancer cell lines, primary tumors, and tissues to detect contaminated cell lines. The approach is based on the intuitive idea that often contamination can be traced back to different tumor types or tissues. We expect that these signatures of contamination can also be found in the cell line's gene expression profile. Our lab already has most tools in place to integrate multiple microarray datasets in a single database. Focus of this project would be on developing the methods for comparing cell line gene expression data with other datasets. The ultimate goal would be a tool to which a researcher can submit his cell line gene expression data and that scores the likelihood of the cell line being contaminated.
    • Technical skills: knowledge of bionformatics in general and specifically the statistical programming language R would be an advantage. Following our MSc level course at the University of Amsterdam on the analysis of genome-wide experiments provides most of the required skills.

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The Bioinformatics Laboratory offers students the possibility to do a bioinformatics project ('stage') to further specialize in this scientific field. The minimum period for a practical period is three months but depends on the background and skills of the student. The student is assumed to have programming and other skills (e.g., statistics, relational databases, bioinformatics) to successfully engage in a project. During the practical period the student is allowed to participate in bioinformatics courses or meetings that are organized by the Bioinformatics Laboratory.

If you are interested then contact Antoine van Kampen (a.h.vankampen@amc.uva.nl) to discuss interests and possibilities.
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See also Research projects for master students (Biological, Biomedical, and life sciences)
 

Trainee projects

  • Bioinformatics analysis of LC-MS metabolomics data
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    • Supervisor: Antoine van Kampen, a.h.vankampen@amc.uva.nl, 020-5667096
    • Description: to be added
    • Technical skills: to be added
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    • Supervisor: Antoine van Kampen, a.h.vankampen@amc.uva.nl, 020-5667096
    • Description: Liquid chromatography coupled to mass spectrometry (LC/MS) is used in metabolomics research. In this context, the technology has been increasingly used for e.g, the discovery of biomarkers. One of the challenges in this domain remains development of better approaches for the bioinformatics analysis of LC/MS data. In this project we aim to develop a processing pipeline for pre-processing and statistical analysis of metabolomics data.
    • Technical skills: The students should have programming skills and interest in bioinformatics. Knowledge and interest in statistical analysis is important. The methods will be developed in the R-statistical package and we will explore the use of Taverna for workflow management.

  • An in-silico approach for the detection of contaminated tumor cell lines
    • Supervisor: Perry Moerland, p.d.moerland@amc.uva.nl, 020-5664660
    • Description: For decades, hundreds of different human tumor type–specific cell lines have been used in experimental cancer research as models for their respective tumors. However, sometimes cell lines that have been used for years turn out to be contaminated (see here for a recent example). In this project you will work on an approach that uses publicly available microarray datasets of human cancer cell lines, primary tumors, and tissues to detect contaminated cell lines. The approach is based on the intuitive idea that often contamination can be traced back to different tumor types or tissues. We expect that these signatures of contamination can also be found in the cell line's gene expression profile. Our lab already has most tools in place to integrate multiple microarray datasets in a single database. Focus of this project would be on developing the methods for comparing cell line gene expression data with other datasets. The ultimate goal would be a tool to which a researcher can submit his cell line gene expression data and that scores the likelihood of the cell line being contaminated.
    • Technical skills: knowledge of bionformatics in general and specifically the statistical programming language R would be an advantage. Following our MSc level course at the University of Amsterdam on the analysis of genome-wide experiments provides most of the required skills.

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Medical Informatics (AMC)
MIK1.2 Biomedische basis principes MIK2.1 Databases en computernetwerken MIK3 DGO-Bioinformatics
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MIK Master, Current issues in medical informatics I MIK GRID International class of Medical Informatics
 
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AMC Graduate School
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  • Analysis of genome-wide expression data, 6 April - 6 May, 2010 (Bioinformatics II)

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DNA technology course , March 2010
 
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The Bioinformatics Laboratory offers students the possibility to do a bioinformatics project ('stage') to further specialize in this scientific field. The minimum period for a practical period is three months but depends on the background and skills of the student. The student is assumed to have programming and other skills (e.g., statistics, relational databases, bioinformatics) to successfully engage in a project. During the practical period the student is allowed to participate in bioinformatics courses or meetings that are organized by the Bioinformatics Laboratory.

If you are interested then contact Antoine van Kampen (a.h.vankampen@amc.uva.nl) to discuss interests and possibilities.
Added:
>
>

Trainee projects

  • Bioinformatics analysis of LC-MS metabolomics data
    • Supervisor: Antoine van Kampen, a.h.vankampen@amc.uva.nl, 020-5667096
    • Description: to be added
    • Technical skills: to be added
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Medical Bioinformatics

Education

Course and training programmes

Medical Informatics (AMC)
MIK1.2 Biomedische basis principes MIK2.1 Databases en computernetwerken MIK3 DGO-Bioinformatics MIK Master, Current issues in medical informatics I MIK GRID (Silvia)
UvA Faculty of Science
BioMedical Sciences/Psychobiology (SILS) Bioinformatics-I (Computational Sciences) Bioinformatics-II (Computational Sciences)    
AMC Graduate School
Introduction to Bioinformatics DNA Technology Proteomics, Mass Spectrometry and Protein Research    
Other
Information for AMC Trainees Introduction to Unix Pattern Recognition (NBIC PhD School) Optimization (NBIC PhD School)  
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Medical Bioinformatics


 
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Upcoming courses

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Courses and training programmes Upcoming courses
Medical Informatics (AMC)
MIK1.2 Biomedische basis principes MIK2.1 Databases en computernetwerken MIK3 DGO-Bioinformatics
MIK Master, Current issues in medical informatics I MIK GRID (Silvia) International class of Medical Informatics
UvA Faculty of Science
BioMedical Sciences/Psychobiology (SILS) Bioinformatics-I (Computational Sciences) Bioinformatics-II (Computational Sciences)
AMC Graduate School
Introduction to Bioinformatics DNA Technology Proteomics, Mass Spectrometry and Protein Research
Other
Introduction to Unix Pattern Recognition (NBIC PhD School) Optimization (NBIC PhD School)
 
  • Analysis of genome-wide expression data, 6 April - 6 May, 2010 (Bioinformatics II)
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Information for traineeship

The Bioinformatics Laboratory offers students the possibility to do a bioinformatics project ('stage') to further specialize in this scientific field. The minimum period for a practical period is three months but depends on the background and skills of the student. The student is assumed to have programming and other skills (e.g., statistics, relational databases, bioinformatics) to successfully engage in a project. During the practical period the student is allowed to participate in bioinformatics courses or meetings that are organized by the Bioinformatics Laboratory.
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Information for trainees
The Bioinformatics Laboratory offers students the possibility to do a bioinformatics project ('stage') to further specialize in this scientific field. The minimum period for a practical period is three months but depends on the background and skills of the student. The student is assumed to have programming and other skills (e.g., statistics, relational databases, bioinformatics) to successfully engage in a project. During the practical period the student is allowed to participate in bioinformatics courses or meetings that are organized by the Bioinformatics Laboratory.
  If you are interested then contact Antoine van Kampen (a.h.vankampen@amc.uva.nl) to discuss interests and possibilities.
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MIK1.2 Biomedische basis principes MIK2.1 Databases en computernetwerken MIK3 DGO-Bioinformatics MIK Master, Current issues in medical informatics I MIK GRID (Silvia)
 
UvA Faculty of Science
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BioMedical Sciences/Psychobiology (SILS) Bioinformatics-I (Computational Sciences) Bioinformatics-II (Computational Sciences)    
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BioMedical Sciences/Psychobiology (SILS) Bioinformatics-I (Computational Sciences) Bioinformatics-II (Computational Sciences)    
 
AMC Graduate School
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Introduction to Bioinformatics DNA Technology Proteomics, Mass Spectrometry and Protein Research    
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Introduction to Bioinformatics DNA Technology Proteomics, Mass Spectrometry and Protein Research    
 
Other
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Information for AMC Trainees Introduction to Unix Pattern Recognition (NBIC PhD School) Optimization (NBIC PhD School)  

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Information for AMC Trainees Introduction to Unix Pattern Recognition (NBIC PhD School) Optimization (NBIC PhD School)  
 

Upcoming courses

  • Analysis of genome-wide expression data, 6 April - 6 May, 2010 (Bioinformatics II)
  • DNA technology course - March 2010
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Information for traineeship

The Bioinformatics Laboratory offers students the possibility to do a bioinformatics project ('stage') to further specialize in this scientific field. The minimum period for a practical period is three months but depends on the background and skills of the student. The student is assumed to have programming and other skills (e.g., statistics, relational databases, bioinformatics) to successfully engage in a project. During the practical period the student is allowed to participate in bioinformatics courses or meetings that are organized by the Bioinformatics Laboratory.

If you are interested then contact Antoine van Kampen (a.h.vankampen@amc.uva.nl) to discuss interests and possibilities.
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Medical Bioinformatics

Education
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Upcoming courses at the Bioinformatics Laboratory

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Medical Informatics (AMC)
MIK1.2 Biomedische basis principes MIK2.1 Databases en computernetwerken MIK3 DGO-Bioinformatics MIK Master, Current issues in medical informatics I MIK GRID (silvia)
UvA Faculty of Science
BioMedical Sciences/Psychobiology (SILS) Bioinformatics-I (Computational Sciences) Bioinformatics-II (Computational Sciences)    
AMC Graduate School
Introduction to Bioinformatics DNA Technology Proteomics, Mass Spectrometry and Protein Research    
Other
Information for AMC Trainees Introduction to Unix Pattern Recognition (NBIC PhD School) Optimization (NBIC PhD School)  

Upcoming courses

 
  • Analysis of genome-wide expression data, 6 April - 6 May, 2010 (Bioinformatics II)
  • DNA technology course - March 2010
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Previous courses

  • Analysis of genome-wide expression data 30 March - 24 April 2009 (Bioinformatics II)
  • DNA technology March 2009
  • Introduction to bioinformatics 9 - 13 February 2009
  • Case study - comparison of protein-coding and non-coding genes 1 September - 24 October 2008 (Bioinformatics I)
  • Advanced bioinformatics 31 March - 16 May 2008
  • DNA technology March 2008
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Bioinformatics Laboratory
Medical Bioinformatics and e-Bioscience
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META TOPICPARENT name="WebHome"
Medical Bioinformatics

Education
 

Upcoming courses at the Bioinformatics Laboratory

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  • Analysis of genome-wide expression data, 6 April - 6 May, 2010 (Bioinformatics II)
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  • Analysis of genome-wide expression data, 6 April - 6 May, 2010 (Bioinformatics II)
  • DNA technology course - March 2010
 

Previous courses

  • Analysis of genome-wide expression data 30 March - 24 April 2009 (Bioinformatics II)
  • DNA technology March 2009
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Bioinformatics Laboratory
Medical Bioinformatics and e-Bioscience
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Bioinformatics Laboratory
Medical Bioinformatics and e-Bioscience

Upcoming courses at the Bioinformatics Laboratory

  • Analysis of genome-wide expression data, 6 April - 6 May, 2010 (Bioinformatics II)

Previous courses

  • Analysis of genome-wide expression data 30 March - 24 April 2009 (Bioinformatics II)
  • DNA technology March 2009
  • Introduction to bioinformatics 9 - 13 February 2009
  • Case study - comparison of protein-coding and non-coding genes 1 September - 24 October 2008 (Bioinformatics I)
  • Advanced bioinformatics 31 March - 16 May 2008
  • DNA technology March 2008
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