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LACDR PhD Portal

Training

As a PhD candidate, you are expected to follow certain courses and training programmes in the context of your training as a researcher. The courses and training programmes will help you conduct your research, write your dissertation, develop your career, and gain self-insight. The LACDR PhD training programme comprises a mandatory part and an optional part.

Education hours

You have to collect a minimum of 280 education hours for education over the course of four years. This number of education hours is divided into:

  • 140 hours of activities focusing on transferable skills and personal development, the so-called personal skills, and
  • 140 hours of academic activities (training in the candidate’s specialism, conference attendance, etc.), the so-called professional skills.

The total programme of courses needs approval from the individual PhD Advisory Committee (PAC).  You are responsible for keeping a record of the courses you have successfully completed in your Education and Supervision Plan (ESP) and to register the courses in LUCRIS GSM.

1. Courses on personal skills – mandatory part organized by the faculty of Science:

1.1 Scientific Conduct (5h)

A number of cases of scientific fraud and misconduct were bad publicity for the scientific world. Are these cases signs of a system in crisis or just some anomalies in an otherwise good scientific framework? This course will give attention to the grey area and your role as an individual scientist to prevent misconduct.

Recommended moment of attendance: start of your third year

For more information, see the website of the Graduate school of Science.

2. Courses on personal skills – mandatory part organized by LACDR:

2.1 PhD Introductory Course on Drug Research (35h)

During the course, you will be introduced to the research objectives of all the divisions of LACDR, to the LACDR organization in general and to the PhD supervision and education program. In addition, other subjects will be discussed, such as:  

  • Partners and collaborations of LACDR
  • Presentation skills
  • Knowledge protection through patents and entrepreneurship
  • Developing your scientific career
  • Networking and social activities

Recommended moment of attendance: in your first year

2.2 Introduction to Teaching & Supervision (28h)

As part of your PhD programme, you are contributing to teaching in the Bachelor and the Master programs in Bio-Pharmaceutical Sciences of Leiden University. This course, organized in collaboration with ICLON, aims at the formulation of learning goals, choosing the format for teaching and learning activities, student motivation and tools for giving feedback.

After the first session, you will prepare two assignments to be evaluated in the second session half a year later:

  1. a Teaching Assignment in a classroom/ laboratory setting;
  2. a Supervision Assignment of a bachelor and/ or master research project.

Recommended moment of attendance: end first year/beginning of your second year

2.3 Data Management Course (14h)

This course is set up in collaboration with the data experts of the Leiden University Library, Participants will be aware of the importance of sound data management by the FAIR principles (Findable, Accessible, Interoperable, Reusable). You will learn about data management requirements and managing your own data according to best practices for responsible data curation common in their field of research. In course period, you will write your own data management plan.

Among others, the following subjects will be discussed:

  • Policy issues for LACDR, Faculty of Science, University and funding organizations
  • Public database search and use
  • Data management and good academic practice issues in science
  • Research data life cycle considerations: from collecting data to publication
  • Electronic lab notebook (ELN)
  • Ethics & legal compliance
  • Tips and information about big data

Recommended moment of attendance: in your first year

For the mandatory LACDR courses, you will automatically receive an invitation. Should you have any questions on the abovementioned courses, do not hesitate to send an e-mail to phd.office@leidenuniv.nl.

3. Courses on personal skills – optional part organized by HRM:

HRM Learning & Development of Leiden University offers PhD training courses, including academic writing, presenting skills, time management, and knowledge utilisation. Courses in career development, personal development, communication, working effectively and research skills are taught throughout the year. Please go to the website for an overview of all available courses on personal skills. Click here for an overview of courses offered by HRM of Leiden University

4. Courses on professional skills – optional part organized by LACDR:

LACDR has developed specialization courses for PhD’s and postdocs of LACDR. Within LACDR, a lot of knowledge on scientific computing and in-depth bio-pharmaceutical technics is available and the experts on these topics are eager to share their knowledge with their colleagues. The courses are tailored to the expertise of the biopharmaceutical research field. To transfer this knowledge, a number of specialization courses are set up. Courses will be announced widely among LACDR employees as soon as a date is set.

The following courses are in the programme:

4.1 Basic Programming in Python (24h)

After following this course, you will be able to use Python to reformat data as needed, calculate summary statistics, and visualize results in publication-ready graphs. Moreover, you will learn the basic structure of the language in such a way that it will become easier to follow more advanced topics such as Bioconductor or more advanced applications. No prior programming experience is required. This course is mainly intended for experimentalists who need a better understanding what tools to select and how to apply these to analyze their data. We focus on basic principles and application of tools. After a brief introduction on programming concepts and language syntax, students will practice with case studies.

During this course the following subjects will be covered: 

  • Python syntax
  • Python data structures
  • Python objects, methods and functions
  • Reading and writing data files
  • Data processing such as reshaping and aggregation
  • Making publication ready graphs

Recommended moment of attendance: before starting your data analysis

4.2 Basic Programming in R (24h)

After following this course, you will be able to use R in your basic data manipulation and visualization tasks. You will be able to reformat data as needed, calculate summary statistics, and visualize results in publication-ready graphs. Moreover, you will learn the basic structure of the language in such a way that it will become easier to follow more advanced topics such as iteration, anonymous functions, and working with list-columns. No prior programming experience is required. This course is mainly intended for experimentalists who need a better understanding what tools to select and how to apply these to analyze their data. We focus on basic principles and application of tools. After a brief introduction on programming concepts and language syntax, students will practice with case studies.

During this course the following subjects will be covered: 

  • R syntax
  • R data structures
  • R objects, methods, and functions
  • R packages and the tidyverse (the best R package)
  • Working with Rstudio and Rmarkdown
  • Reading and writing data files
  • Data processing such as reshaping and aggregation
  • Making publication-ready graphs

Recommended moment of attendance: before starting your data analysis

4.3 Computational Chemical Biology (40h)

Chemical Biology explores biology via chemical tools. In practice this means that the molecular interaction space of protein targets is probed. Computational Chemical Biology is the computational addition to these goals and is located in between the fields of medicinal chemistry, cheminformatics, bioinformatics, and computational biology. This course consists of bio- and cheminformatic approaches which are coupled to structure-based methods using crystal structures. Herein, students learn to computationally analyze protein sequences as well as ‘small molecules’, and ultimately model interactions between them using publicly accessible databases and state of the art tools. 

After this course, you will be able to:  

  • Explain which type of research questions can be considered using cheminformatics, bioinformatics, and structure-based drug discovery. 
  • Explain methods that are typically used in cheminformatics, bioinformatics and structure-based drug discovery (e.g., descriptors, machine learning approaches, crystal structure and homology models). 
  • Explain limitations (either caused by lack of data or by lack of confidence) in cheminformatics, bioinformatics or structure-based drug discovery 
  • Interpret results from studies in which small molecules are docked to crystal structures and in which quantitative structure-activity relationship (QSAR) models are employed. 

Recommended moment of attendance: no particular moment

4.4 Population Pharmacokinetic Modeling (28h)

With population pharmacokinetic (PK) modeling we describe the concentration-time profile of a drug in the body. An important aspect of population PK modeling is identifying sources of variability between individuals of a population and quantifying this inter-individual variability. In a subsequent covariate analysis, potential patient and treatment characteristics that can explain (part of) the inter-individual variability are investigated. Once we have a population PK model that can describe and predict both general trends in the PK of drugs and individual deviations from those trends, we can use model-based simulations to optimize drug dosing. With the simulations, we can identify characteristics that can put patients at risk for overdosing, leading to undesired side-effects, or underdosing, leading to therapy failure. The model-based simulations can then be used to individualize drug dosing recommendations for these patients, based on their characteristics.    

After this course, you will be able to:    

  • Explain how PK processes are parameterized and how models are used to describe and predict concentration-time profiles  
  • Develop structural population PK models to describe the relationships between drug dosing and drug concentration.  
  • Identify and quantify inter-individual variability and covariate relationships in population PK models.  
  • Explain how covariates can be used to (partially) explain inter-individual variability in population PK models.  
  • Explain how model-based simulations can be used to optimize and individualize drug dosing regimen, as well as perform these simulations yourself.  

Recommended moment of attendance: no particular moment

Should you have any questions on the abovementioned courses, do not hesitate to send an e-mail to phd.office@leidenuniv.nl.

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