PhD Course Management

Short description

As a part of their teaching responsibilities, PhD-students sometimes act as co-supervisors for undergraduate students’ bachelor projects, written assignments, master thesis, or research year assignments. In doing so, the PhD-students play an important role as a near-peer-supervisor guiding the students in their learning. This course aims to prepare PhD-students for their role as co-supervisors on undergraduate students’ assignments and/or learning processes. The course is relevant for all PhD students who wish to improve the quality of their supervision of undergraduate students.

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The course introduces participants to R and RStudio as tools for data management, basic programming, and data visualisation. It aims to provide the fundamental skills needed to write clear and reproducible R scripts, handle common data structures, and perform simple descriptive and graphical analyses. The course focuses on programming principles and reproducible workflows rather than statistical theory.

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Half-way in your PhD, you now know what being a PhD student really is about. It is also a turning point where questions such as “what options do I have”, “how can I make the most of my doctoral degree” and  “how do I make a sensible choice for my future career “ may start to pop up and cause both excitement and concern. This workshop aims at giving you the tools to answer those questions and to support you in being able to make an informed choice for the next step in your career. In the workshop, you will consider 1. your professional development so far and whether there competences you would like to improve during your part b, and 2. How you can start preparing for the transition by building your network, understanding your options and increasing your awareness of what a “good” job/work life is to you.

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The course aims to provide you with an idea about current methodologies, topics, and, not at least, principle investigators with research interests that could potentially help you on with your own project.

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To give students practical experience in using advanced AI methods for scientific writing tasks; the ability to respond efficiently to reviewer feedback using AI; skills to synthesise multiple scientific papers into a coherent PhD thesis or grant applications; the ability to challenge existing knowledge and develop new ideas using AI.

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This course aims to assist and support in navigating the application process for both small and large grant applications. This task can be particularly challenging for PhD and research year students.

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To create a framework for integrating project management tools into the interaction between supervisors and Ph.D. students.  The content includes a post assignment asking you to work together with your advisor, agreeing on how to use these tools in your communication about progress.  These tools enable you both to integrate planning for key decision points related to your Ph.D. project.  In addition, you will be introduced to the PDCA framework, which stands for Plan-Do-Check-Act.  This framework will be used to reflect on how to optimize your independent work through Plan-Do; and your work with others through Check-Act.

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The course provides detailed knowledge on flow cytometry, with a thorough review of the theoretical foundation necessary for performing state-of-the-art flow cytometry experiments, and includes extensive practical hands-on data analysis exercises covering the most important topics and pitfalls. Thus, the course provides the participant with a broad understanding of how to design multicolour flow cytometry experiments, how to handle sample material, and how to analyse and present flow cytometry data for publication.

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The aim of this course is to introduce research data management. Finding, collecting, analyzing, storing and sharing data are central activities in research. Research Data Management (RDM) and FAIR (Findability, Accessibility, Interoperability, Reusability) principles are thus becoming increasingly important, and researchers are often required by institutions and funders to prepare a data management plan.

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The aim of the course is to ensure that the participants can convey and handle responsible research communication of their own PhD project. Through different media channels and via different presentation techniques, the participants will be able to explain and present complex research in an engaging and understandable way to an audience whether it be the general public, journalists, patients, students in a classroom or a foundation’s review committee.

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The overall aim is to give the participants understanding of cardiovascular physiology and methods of assessment. The course will provide the participants with the knowledge to choose the optimal methods for their individual research projects based on the methods’ strengths and weaknesses.

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The aim of the course is to:

1. Provide students with the state-of-the-art knowledge on genome engineering and CRISPR gene editing technology
2. Introduce the different types of CRISPR gene editing tools (e.g., base editing, prime editing) and their applications
3. Provide trainings on CRISPR designing with high on-target efficiency and low off-target
4. Provide trainings on design CRISPR and donor vectors for gene knockin, tagging
5. Introduce CRISPR data science, CRISPR AI, and high-throughput CRISPR computing
6. Provide trainings on analysing and understanding CRISPR editing data with TIDE, ICE and NGS
7. Provide theocratical trainings and education on CRISPR gene therapy
8. Introducing state-of-the-art methods for CRISPR delivery
9. Provide laboratory trainings on CRISPR-mediated gene knockout and knockin in cultured cell lines.
10. Prepare PhD students with essential knowledge, tools and skills for their gene editing projects

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Epidemiologic measures of occurrence and association, study designs - including macro-epidemiological, cross-sectional, case-control and cohort studies as well as experimental, randomized controlled trials - interpretation and inference, including random error, selection and information bias, confounding and effect modification. A specific session will deal with causation. 

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Structure and content of the databases will be reviewed. With respect to literature searching, emphasis will be on PubMed, and the participants will learn to construct precise search strings by employing the tools (e.g. MeSH) of the databases. The focal point of the introduction to Web of Science and Scopus will be cited reference searches. In continuation of this, issues of “What is scientific quality, and can it be measured?” (‘impact factor’, H-index, research registration, etc.) will be discussed. Furthermore, topics related to the publication of research results will be presented and discussed (Responsible conduct of research, Copyright, Open Access, and Predatory Journals). Finally, reference management programs (EndNote) will be introduced.

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The aim of this course is to train the students’ general and disciplinary writing ability to enhance successful disciplinary writing in conformity with contextual/situational, discoursal and genre- and text-type conventions and requirements of written academic English in health sciences.

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The aim of this course is to train the students’ general and disciplinary writing ability to enhance successful disciplinary writing in conformity with contextual/situational, discoursal and genre- and text-type conventions and requirements of written academic English in health sciences.

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The overall aim of this course is to provide participants with a hands-on, practical approach to Cochrane Systematic review methodology. Specifically, the course will focus on 1) understanding the key principles of building a structured and relevant search strategy, and selecting studies based on a predefined protocol, 2) assessing the risk of bias in included studies, 3) acquiring knowledge about how to effectively present the results of bias assessment, 4) gaining practical experience with Covidence software and the RoB2 tool.

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A welcome event for all newly enrolled PhD students to the PhD programme to give  them the opportunity to meet and interact with fellow PhD students and AU representatives from fields relevant during the PhD programme.

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To give students practical experience in using advanced AI methods for scientific writing tasks; the ability to respond efficiently to reviewer feedback using AI; skills to synthesise multiple scientific papers into a coherent PhD thesis or grant applications; the ability to challenge existing knowledge and develop new ideas using AI.

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On behalf of the Cardiovascular Network at Health, we are offering a 3 ECTS course in grant writing, targeted towards junior researchers, who seek to apply for research funding to support their studies. The course will provide a forum of collegial cooperation and support for the grant submissions, where the student’s main supervisor is also expected to be heavily involved.

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To provide students with an overview on how to take a research idea through to implementing, analysing, and interpreting an epidemiologic study using the Danish Clinical Cancer Databases, and Denmark’s network of medical and population-based registries. Each concept will be introduced in a lecture. Through a combination of exercises and computer labs, students will be provided with guidance on implementing, analysing and interpreting studies using data from the clinical cancer databases. Students are not required to complete an exam at the end of the course.

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This PhD course aims to provide a comprehensive introduction to the principles, developments, and applications of single-cell and spatial OMICS technologies. Students will gain fundamental knowledge of sequencing and profiling methods used to study genomics, epigenomics, transcriptomics, metabolomics, and proteomics at both single-cell and spatially resolved levels.

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This course enables a qualified debate about the relevance and applicability of current knowledge on stress and resilience to advance mental health and clinical practice. 

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The course covers the principles of Good Clinical Practice (GCP) and the roles of Sponsor, Investigator, Monitor, the ethics committee and the regulatory authorities.

Focus will be on the practical aspects on how to comply with the principles of Good Clinical Practice (GCP) and requirements in the national regulations in Denmark for Clinical trials with medicinal products.

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This course aims to impart knowledge about and give participants an introduction to and practical experience using artificial intelligence (AI) tools to enhance their scientific and academic writing process.

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To focus on knowledge that is critical for success in an academic research career. With a point of departure in the PhD students’ present and future career goals, this course explores key building blocks of successful academic research careers. These building blocks include networking, interdisciplinarity and groundwork for grant-based funding opportunities.  These building blocks are used to create a career canvas that connects PhD work to the preparation needed for successful Post Doc experiences. The purpose of the career canvas is to Iay groundwork for recognizing opportunities and acting on them. This course is applicable for Health PhD students who want to become either part-time researchers and clinicians or full-time researchers.

Short description

Doctoral  students can be stressed, and many experience a need for more productivity. Using simple tools and establishing good writing habits can make the PhD and the research process more enjoyable and rewarding. The writing workshop focuses on you and your challenges; you have time to write your own text. Please bring your computer and a text that you are writing on at the moment!

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To give students practical experience in using advanced AI methods for scientific writing tasks; the ability to respond efficiently to reviewer feedback using AI; skills to synthesise multiple scientific papers into a coherent PhD thesis or grant applications; the ability to challenge existing knowledge and develop new ideas using AI.

Short description

To give students practical experience in using advanced AI methods for scientific writing tasks; the ability to respond efficiently to reviewer feedback using AI; skills to synthesise multiple scientific papers into a coherent PhD thesis or grant applications; the ability to challenge existing knowledge and develop new ideas using AI.

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The aim of the course is to provide an introduction to the design, analysis, and interpretation of time-to-event data.

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Provide an overview of the different aspects of conducting clinical research

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The aim of the course is to introduce the main research fields in health sciences and to give an introduction to the requirements for conducting a research year or a PhD at HEALTH.

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The course aims to provide the background for critical reading and interpretation of epidemiological research articles.

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The course is designed to strengthen participants’ understanding of R as a programming language and to support the development of efficient, reproducible, and well-structured code. The focus is on programming skills, workflow management, and the use of tools and packages that streamline coding and reporting.

The course does NOT cover statistical methods.

Short description

As a part of their teaching responsibilities, PhD-students sometimes act as co-supervisors for undergraduate students’ bachelor projects, written assignments, master thesis, or research year assignments. In doing so, the PhD-students play an important role as a near-peer-supervisor guiding the students in their learning. This course aims to prepare PhD-students for their role as co-supervisors on undergraduate students’ assignments and/or learning processes. The course is relevant for all PhD students who wish to improve the quality of their supervision of undergraduate students.

Short description

Doctoral students can be stressed, and many experience a need for more productivity. Using simple tools and establishing good writing habits can make the PhD and the research process more enjoyable and rewarding. The writing workshop focuses on you and your challenges; you have time to write your own text. Please bring your computer and a text that you are writing on at the moment!

Short description

This advanced PhD course is designed for health science students interested in understanding and exploring how genome-wide association studies (GWAS) data can provide deeper biological insights. The course provides theoretical knowledge and hands-on experience in GWAS and post-GWAS analytical approaches, focusing on the integration of genetic results with functional genomics data to identify affected genes, tissues and cell types, and to uncover the biological mechanisms underlying the diseases or phenotype of interest. The course further expands on the use of GWAS in the context of polygenic risk scores (PRSs) in disease prediction.

Students will learn to work with publicly available GWAS summary statistics and pre-trained machine learning models for complex traits such as metabolic traits (e.g. obesity, type 2 diabetes) and psychiatric conditions (e.g. ADHD, depression). The course emphasizes how to analyse post-GWAS data, interpret findings, through analyses using a variety of R-based, Linux-based, and web-based tools.

The main focus will be on secondary GWAS analyses, but students will also learn the principles of how to do the primary GWAS.

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The aim of the course is to increase your personal and professional success by learning how to lead yourself, your project and your important relations.

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The aim of this course is to impart knowledge about and give course participants an introduction to academic text production in English, applying textual and discourse strategies and methodologies in accordance with the current conventions of written academic English in health sciences.

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The interpretation of research findings is often non-trivial. This course enables doctoral students to apply core principles of measurement theory (MT) to their own biomedical research, helping them evaluate data with confidence, avoid interpretation errors, and justify methodological decisions. Students will learn how foundational MT principles support rigorous analysis and informed decision-making, applying these concepts directly to a cohort, model, experiment, or measurement used in their own work.

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By the end of the course, the PhD student will:

• Be familiar with the Danish Code of Conduct for Research Integrity as well as Aarhus University guidelines and Health standards of Responsible Conduct of Research
• Be able to understand and discuss principles of research integrity and responsible conduct of research
• Be able to identify, analyse and discuss cases of scientific misconduct and questionable research practices in the grey zone between misconduct and poor science
• Know where to seek advice concerning responsible conduct of research

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The aim of this course is to impart knowledge about and give course participants an introduction to academic text production in English, applying textual and discourse strategies and methodologies in accordance with the current conventions of written academic English in health sciences.

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To give students practical experience in using advanced AI methods for scientific writing tasks; the ability to respond efficiently to reviewer feedback using AI; skills to synthesise multiple scientific papers into a coherent PhD thesis or grant applications; the ability to challenge existing knowledge and develop new ideas using AI.

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The course provides the students with basic skills using the most common, simple statistical models and analyzes in medical research.

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A welcome event for all newly enrolled PhD students to the PhD programme to give  them the opportunity to meet and interact with fellow PhD students and AU representatives from fields relevant during the PhD programme.

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Mandatory for main supervisors

The quality of PhD students’ education is partly dependent on the PhD supervisor’s competencies as a supervisor. The aim of this course in PhD supervision is to expand the participants’ repertoire of supervision strategies and methods to provide a flexible approach to supervision, strengthen their reflections on practices, roles, and relationships in the supervision process, and share experiences and new knowledge for advancing PhD supervisors’ competencies.

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The aim of the course is to introduce PhD students at Health, Aarhus University to the basic terms and principles of GMP controlled manufacture of pharmaceutical products.

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To create a framework for integrating project management tools into the interaction between supervisors and Ph.D. students.  The content includes a post assignment asking you to work together with your advisor, agreeing on how to use these tools in your communication about progress.  These tools enable you both to integrate planning for key decision points related to your Ph.D. project.  In addition, you will be introduced to the PDCA framework, which stands for Plan-Do-Check-Act.  This framework will be used to reflect on how to optimize your independent work through Plan-Do; and your work with others through Check-Act.

Short description

The course will provide didactic and plenum discussions on cancer diseases and research. The theoretical lectures will include insight into the disease, methodology for research, progression to clinical trials, and common errors that hamper project development. The course will have a strong focus on the progression of a project from basic research to clinical application. Furthermore, students are asked to present their projects, including a critical perspective and applied new possibilities for the projects to reach a higher impact of their work.

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The course aims to provide a comprehensive overview of the methodologies used across the cardiovascular research disciplines. The course will teach critical aspects of designing, conducting, and interpreting cardiovascular research within basic, epidemiological, and clinical research.