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DTSTART;VALUE=DATE:20251208
DTEND;VALUE=DATE:20251210
DTSTAMP:20260414T212132
CREATED:20251112T114614Z
LAST-MODIFIED:20251112T114614Z
UID:10001489-1765152000-1765324799@ddsa.dk
SUMMARY:4th symposium on the advances in biomedical engineering and neuroscience (2025)
DESCRIPTION:Welcome to 4th symposium on the advances in biomedical engineering and neuroscience \nProgram: BEN ***mandatory course*** \nDescription: \nThe course has a focus on disseminating the most relevant and recent achievements within biomedical and engineering science to address relevant health care problems. This course will be organized annually and will include a series of lectures from internationally recognized speakers and from speakers from Aalborg University that are experts within the field. During the symposium\, some time will be dedicated to the facilitation of interaction between the speakers and participants. The detailed agenda of the course will be provided on the course web site. Main topics can include electrophysiology\, psychophysics\, bio-signal processing\, biostatistics\, rehabilitation technology\, machine learning\, physiological modeling\, decision support\, big data\, image analysis and computational neuroscience but also topics relevant for the life as a PhD student such as management of a research project\, risk analysis and mitigation etc. \nPrerequisites: \nThe course targets PhD students of the Program in Biomedical Engineering and Neuroscience and other PhD students working within these areas. The course is repeated every year with a different focus\, thus participation to the previous edition does not preclude participation in this edition. \nForm of evaluation: \nA poster session will be organized during the symposium. PhD students will bring and present a poster on their own work (it is allowed to bring a poster presented at another conference or meeting). \nKey literature: \nRelevant journal articles and book chapters related to the specific talks will be announced shortly before the course. \nOrganizer: Associate Professor Sabata Gervasio\, email: saba@hst.aau.dk \nLecturers: Invited lecturers and lecturers from Department of Health Science and Technology and Clinical Medicine \nECTS: 1.5 \nTime: 8 and 9 December 2025 (08.15-16.15) \nPlace: Aalborg University \nZip code: 9220 \nCity: Aalborg \nMaximal number of participants: 25 \nDeadline: 17 November 2025 \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/4th-symposium-on-the-advances-in-biomedical-engineering-and-neuroscience-2025/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251117
DTEND;VALUE=DATE:20251120
DTSTAMP:20260414T212132
CREATED:20251112T113912Z
LAST-MODIFIED:20251112T113912Z
UID:10001491-1763337600-1763596799@ddsa.dk
SUMMARY:Components of causal inference with focus on assumptions and confounding control (2025)
DESCRIPTION:Welcome to Components of causal inference with focus on assumptions and confounding control (2025) \nProgram: Epidemiology & Biostatistics ***mandatory course **** \nDescription: \nPurpose: You already know that establishing a causal relationship is distinct from observing an association. While individuals who receive the flu vaccine tend to have a lower mortality rate compared to those who do not\, we must consider whether this lower mortality is directly attributable to the vaccine or if it arises from other distinctions between the vaccinated and unvaccinated groups. The concept of confounding introduces a pervasive bias when we compare groups that are not fundamentally similar. It represents a substantial challenge to drawing accurate causal conclusions from observational data. Consequently\, the course’s primary focus revolves around the essential task of mitigating confounding in epidemiological research using various techniques. \nCourse objectives: This course focus on models for confounding control (or adjustment)\, their application to epidemiologic data\, and the assumptions required to endow the parameter estimates with a causal interpretation. The course introduces participants to a set of methods for confounding control with focus on survival analysis: methods that require measuring confounders and how this could be applied in perspective to the research question of interest. Specifically\, the course introduces aspects of directed acyclic graphs\, outcome regression\, propensity score methods\, and inverse-probability weighting of marginal structural models as means for confounding control\, and how this can be implemented and analysed in standard statistical software. \n(Mandatory course for AAU PhD programme Epidemiology & Biostatistics) \nPrerequisites: \nBasic training in epidemiology required (eg.\, the AAU course “Epidemiology – Basic principles” or similar). Basic statistics and basic programming abilities with Stata or R. All participants must bring a laptop with either Stata or R installed. \nKey literature: \nOrganizer: \nPeter Brønnum Nielsen\, PhD\, Assoc. Prof.\, Department of Clinical Medicine\, AAU \nLecturers: Søren Paaske Johnsen\, Peter Brønnum Nielsen\, Chalotte W. Nicolajsen\, +additional \nECTS: 2.5 \nTime: 17\, 18\, 19 November 2025 \nPlace: SUND building\, Aalborg University\, Selma Lagerløfs Vej 249 \nZip code: 9260 \nCity: Aalborg/Gistrup \nMaximal number of participants: 25 \nDeadline: 27 October 2025 \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/components-of-causal-inference-with-focus-on-assumptions-and-confounding-control-2025/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251006
DTEND;VALUE=DATE:20251009
DTSTAMP:20260414T212132
CREATED:20250312T150357Z
LAST-MODIFIED:20250312T150416Z
UID:10001490-1759708800-1759967999@ddsa.dk
SUMMARY:Clinical cancer research – from laboratory to bedside
DESCRIPTION:Welcome to clinical cancer research – from laboratory to bedside \nProgram: B \nDescription: \nThis PhD course is offered by the Doctoral School of Medicine\, Biomedical Science and Technology\, Aalborg University and Clinical Cancer Research Center\, Aalborg University Hospital\nThis PhD course is aimed at PhD students working within the field of translational and clinical cancer research. The course will touch on several topics related to translational cancer research\, including basic science methodologies\, statistics and bioinformatics\, and the key points in clinical trials. The basic science lectures touch upon molecular pathology\, tumour heterogeneity\, molecular biology as well as the currently used methods to elucidate these topics. The bioinformatic lectures will focus on workflows for personalized medicine\, interpretation of sequencing data\, and clinical implications. Data management\, AI and meta research will be a part of these lectures. The clinical lectures will discuss current clinical questions and evidence for PM. \nParticipation in the course is free for PhD students. However\, transportation costs are not covered and must be settled by the PhD student him/herself. \nOrganisers: \nAssociate Professor Laurids Østergaard Poulsen\, email: laop@rn.dk \nAssociate Professor Stine Dam Henriksen email: stdh@rn.dk \nSecretary assistant Lise Tordrup Elkjær\, lit@rn.d \nClinical Cancer Research Center\, Aalborg University Hospital \nLecturers: \nStine Dam Henriksen\, Laurids Ø. Poulsen\, Ole Thorlacius-Ussing\, Morten Ladekarl\, Weronika Szejniuk\, Lone Sunde\, Ida Holm\, Lykke Grubach\, Martin Bøgsted\, Mads Sønderkær\, Hanne Due\, Britt E. Laursen\, Bent Honoré\, Helle D. Zacho\, Benjamin Emil Stubbe \nECTS: 2.25 \nTime: 6\,7 and 8 October 2025\, Three-day residential course \nPlace: Comwell Rebild Bakker \nZip code: 9520 \nCity: Skørping \nMaximal number of participants: 15 \nDeadline: 15 September 2025 \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/clinical-cancer-research-from-laboratory-to-bedside-2024/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250930
DTEND;VALUE=DATE:20251004
DTSTAMP:20260414T212132
CREATED:20250312T113818Z
LAST-MODIFIED:20250312T113946Z
UID:10001495-1759190400-1759535999@ddsa.dk
SUMMARY:Ethnography in health care sciences
DESCRIPTION:Welcome to Ethnography in health care sciences \nPhD Programme: Health Care\, Health Promotion and Organizations (HCHPO) \nDescription: \nEthnographic research has become increasingly popular in health care sciences. This course provides a solid methodological foundation for using ethnographic research to understand real-world issues in context. Attention will be paid to how ethnographic strategies can be deployed in health care contexts\, as a way of illustrating the more general principles of ethnographic work. \nOver four days\, this course will cover core components of the ethnographic approach such as data gathering in fieldwork (participant observation\, interview\, field notes and documents)\, data analysis\, ethical issues and writing articles based on ethnography. The course will provide students with an understanding of how to design and carry out ethnographic research and an awareness of contemporary developments in the theory and practice of ethnographic studies. \nThe course is designed for participants and lecturers to engage in different activities such as lectures\, practical exercises and discussions of some of the theoretical\, methodological and practical issues and challenges in using ethnography. Each participant will give a short presentation of his/hers PhD project and receive feedback from lecturers and participants during the course. \nPrerequisites: \nParticipants must provide an abstract or a brief description (approx. 1 page) of a specific issue or challenge related to ethnography concerning their study. This should be sent to hht@rn.dk no later than 15. sept \nEach participant must also prepare a presentation that will be presented at the course (10 minutes)\, on the project also stating what are the points that each wants to have a discussion on. Based on the presentation\, a discussion with lectures and the other participants will follow (each participant has in total aprx 30 minutes). \nOrganizer: Helle Haslund-Thomsen \nLecturers: \nEmma Balkin (EB)\, Birgitte Lerbæk(BIL)\, Siri Lygum Voldbjerg (SLV)\, Britt Laugesen (BL) Kirsten Schultz Petersen (KSP)\, Helle Haslund Thomsen (HHT). \nECTS: 3.0 \nTime: 9-16\, 30 September – 3 October 2025 \nPlace: Selma Lagerløfsvej 249 \nZip code: 9260 \nCity: Aalborg \nMaximal number of participants: 30 \nDeadline: 9 September 2025 \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/ethnography-in-health-care-sciences-2025/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250616
DTEND;VALUE=DATE:20250620
DTSTAMP:20260414T212132
CREATED:20250310T102745Z
LAST-MODIFIED:20250310T102745Z
UID:10001493-1750032000-1750377599@ddsa.dk
SUMMARY:Development and implementation of machine learning models for dynamic risk prediction models in health care applications
DESCRIPTION:Welcome to Development and implementation of machine learning models for dynamic risk prediction models in health care applications \nPhD Programme: Epidemiology and Biostatistics \nDescription: \nTraditional risk prediction generates a risk estimate at a defined timepoint a patient’s trajectory\, for example the risk of death within 30 days following a surgical procedure.\nIn contrast\, dynamic risk prediction enables prediction of risk at any time point. This allows to continuously monitor a patient’s risk profile and forms the basis for intervention if the predicted risk increases.\nIn this course\, we will explore methodological and technical solutions\, as well as corresponding challenges\, for developing and implementing such solutions in health care.\nThe course includes the following topics: \n1) Data management: This part of the course considers the challenges of preparing heterogenous longitudinal health data for prediction.\nWe will cover the various steps involved in this process\, including data formatting\, feature engineering\, and splitting strategies for model validation.\nThis will include discussion about how to handle irregularly sampled health data\, data leakage\, class imbalance\, temporal robustness\, normalisation\, and other potential biases. \n2) Modelling: In this part of the course\, participants will be led through the process of building such models.\nWe will introduce both basic and more advanced dynamic machine learning prediction algorithms\, such as gradient tree boosting\, random forest\, and LSTM and discuss issues related to performance metrics and hyperparameter optimization\, for example Bayesian optimization. \n3) Implementation: In the last part of the course\, we will consider the challenges associated with the implementation of predictive tools in the clinic.\nThis includes technical aspects about hosting\, user interface\, and access to live data\, including an introduction to the FHIR standard.\nRegulatory and organisational issues will also be discussed. During the project the participants will get hands on experience covering realistic scenarios related to the subjects discussed.\nThis will include data management of representative data sets\, training models and hands on introduction to the FHIR set-up. \nKey literature: \nTomašev\, N. et al. Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records. Nature Protocols (2021) doi:10.1038/s41596-021-00513-5. \nVesteghem\, C. et al. Dynamic risk prediction of 30-day mortality in patients with advanced lung cancer: Comparing five machine learning approaches. Under review. \nOrganizer: \nAssistant Professor Heidi Søgaard Christensen\, hschr@dcm.aau.dk \nProfessor Martin Bøgsted\, martin.boegsted@rn.dk \nLecturers: \nAnne Krogh Nøhr\, Charles Vesteghem\, Heidi Søgaard Christensen\, Hendrik Knoche\,\nIda Burchardt Egendal\, Mads Lause Mogensen\, Rasmus Brøndum\,\nSigne Bjerregaard-Michelsen\, Simon Christian Dahl \nECTS:  3.0 \nTime: 16. June\, room 14.01.004\, 17.\, 18.\, 19. June room 12.01.004. \nPlace: Aalborg University\, SUND AAU \, Selma Lagerløfs Vej 249. \nZip code: 9260 \nCity: Gistrup \nMaximal number of participants: 50 \nDeadline: 26 May 2025 \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/development-and-implementation-of-machine-learning-models-for-dynamic-risk-prediction-models-in-health-care-applications/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250501
DTEND;VALUE=DATE:20250503
DTSTAMP:20260414T212132
CREATED:20250305T141402Z
LAST-MODIFIED:20250305T141402Z
UID:10001465-1746057600-1746230399@ddsa.dk
SUMMARY:Method comparison reliability and agreement (2025)
DESCRIPTION:Welcome to Method comparison reliability and agreement 2025 \nProgram: BEN (also relevant for B\, CPM\, CSLTM\, HES) \nDescription: \nThe course will focus on the importance of method comparison studies when evaluating new clinical and experimental methods. The course will describe how method comparison studies are designed and how obtained results are analysed and described. Application of analytical measures such as Coefficient of Variance\, Intra-Class Correlation\, differences in means\, and Bland-Altman’s limits of agreement\, inter-rater reliability\, test accuracy and sample size estimation will be discussed. The aim of the course is to provide the participants with a toolbox that enables them to perform and analyse method comparison studies. This advanced course in biostatistics assumes knowledge of basic methods in biostatistics\, including the concepts of hypothesis testing\, and basic study designs. The course is designed for researchers working in both clinical and experimental settings. \nPrerequisites: \nBasic knowledge on statistics (e.g. Biostatistics I) \nForm of evaluation: \nShort project/assignment \nKey literature: \nAtkinson G\, Nevill A. Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine.  Sports Med 1998; 26: 217-238\, \nWeir JP. Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. Journal of Strength and Conditioning Research 2005; 19: 231–240 \nBland JM\, Altman DG. Measuring agreement in method comparison studies. Statistical Methods in Medical Research 1999; 8: 135 – 160 \nAnd links\, datasets and handouts distributed prior to and during the course. \nOrganizer:\nAssociate Professor Carsten Dahl Mørch\, email: cdahl@hst.aau.dk \nLecturers:\nResearch Assistant Felipe Rettore Andreis \nAssociate Professor Carsten Dahl Mørch \nECTS: 1.5 \nTime: 1 and 2 May 2025\, 08.15-16.15 \nPlace: Aalborg University \nZip code: 9220 \nCity: Aalborg \nMaximal number of participants: 25 \nDeadline: 10 April 2025 \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/method-comparison-reliability-and-agreement-2025/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241002
DTEND;VALUE=DATE:20241005
DTSTAMP:20260414T212132
CREATED:20240424T085639Z
LAST-MODIFIED:20240424T085639Z
UID:10001134-1727827200-1728086399@ddsa.dk
SUMMARY:Big data - from raw data to data integration in clinical research projects (2024)
DESCRIPTION:Welcome to: Big data – from raw data to data integration in clinical research projects \nProgram: Main CSLTM \n·        Biomedicine (B) \n·        Clinical and Pharmacological Medicine (CPM) \n·        Clinical Science\, Laboratory and Translational Medicine (CSLTM)  \nDescription: Omics technologies including genomics\, transcriptomics\, epigenetics\, proteomics and metabolomics are now key technologies in multiple fields of research and a highly active domain in health and medical investigation. Omics is an interdisciplinary research field that coalesces researchers from many different areas of biomedical research into one of the most likely disciplines to successfully foster the translation of basic scientific knowledge into clinical applications for the benefit of patients. In most clinical projects a wide range of clinical assessment data are available e.g. gender\, BMI\, blood and immunological assays as well as Omics based “bigdata”. \nThis PhD course will focus on basic-to-advanced bioinformatics workflows of Omics data management and processing in Life & Medical sciences. A key focus will be to address statistical and computational workflows needed to integrate drylab and wet lab based data for tables and visualization for research reports and publicaitons. \nLiterature/Requirements: Prior to the PhD course a package of literature and an R tutorial must be completed \nPrerequisites: None; Basic R skills \nEvaluation: Active participation in the theoretical and experimental course. \nOrganizer: Allan Stensballe \n Lecturers: Allan Stensballe & Christopher Aboo \n ECTS: 2 \n Time: 2-4 October 2024 \n Place: TBA \n Zip code: \n City: \n Number of seats: 25 \n Deadline: 11 September 2024 \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/big-data-from-raw-data-to-data-integration-in-clinical-research-projects-2024/
LOCATION:Aalborg University                TBA\, Aalborg
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240624
DTEND;VALUE=DATE:20240627
DTSTAMP:20260414T212132
CREATED:20240424T085630Z
LAST-MODIFIED:20240424T085630Z
UID:10001133-1719187200-1719446399@ddsa.dk
SUMMARY:Fundamentals of Clinical Data Science (2024)
DESCRIPTION:Welcome to Fundamentals of Clinical Data Science \nPhD Program: Biomedicine (B) \nDescription: Clinical data science can be defined as the scientific field\, which turns healthcare data into clinically useful applications. This course will introduce the disciplines involved in the full value chain of clinical data science\, covering the transformation of data to model and to applications\, with an aim of giving an overview and understanding of the processes\, rather than how to perform them.  \nThe course is organized into three major themes: \n1) Data sources: The first part of the course covers the management and collection of data from both public sources\, national registries or trough case report forms designed for a study. We will introduce both how to access data\, how to handle privacy concerns (GDPR) and how to make your own data useable for others (FAIR principles). \n2) Modelling: The second part of the course teaches how to transform the collected data from possibly multiple sources to input for a predictive model\, and how to train and validate a model using techniques such as classification\, regression\, or clustering. \n3) From model to clinic: The final part of the course deals with turning a validated model into a clinical decision support system to strengthen operational excellence in value-based health care. How do we ensure that data is available in real time? What legal barriers or ethical issues are involved when a medical decision is guided by artificial intelligence? \n  \nLiterature/Requirements: Students are expected to have some experience with collecting and analyzing health care data. Suggested reading: Kubben\, P.\, Dumontier\, M.\, Dekker\, A. (editors) Fundamentals of Clinical Data Science. Springer Open\, 2019. Available online at:  link TBA \n  \nOrganizers: \n\nAssociate Professor Rasmus Froberg Brøndum\, rfb@dcm.aau.dk\nProfessor Martin Bøgsted\, m_boegsted@dcm.aau.dk\nAssociate Professor Louise Pape-Haugaard\, lph@hst.aau.dk\n\nLecturers: \n\nAssociate Professor Rasmus Froberg Brøndum\, rfb@dcm.aau.dk\nAssistant Professor Charles Vesteghem\nAssociate Professor Louise Pape-Haugaard\, lph@hst.aau.dk\nProfessor Thomas Moeslund\nAssociate Professor Lasse Riis Østergaard\nSenior Statistician Jan Brink Valentin\nChief Executive Officer Mads Lause Mogensen\nOther experts\n\n ECTS: 2.5 \n Time: 24\, 25 and 26 June 2024 (08:15-16:15) \nPlace: TBA \n Zip code: 9220 \n City: Aalborg \n Number of seats: 30 \n Deadline: 3 June 2024 \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/fundamentals-of-clinical-data-science-2024/
LOCATION:Aalborg University                TBA\, Aalborg
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240521
DTEND;VALUE=DATE:20240525
DTSTAMP:20260414T212132
CREATED:20240424T082741Z
LAST-MODIFIED:20240424T082741Z
UID:10001180-1716249600-1716595199@ddsa.dk
SUMMARY:Electrocardiography: Basic Physiology\, Epidemiology\, and Bedside
DESCRIPTION:Welcome to: Electrocardiography: Basic Physiology\, Epidemiology\, and Bedside\nOrganizers: \nClaus Graff\, Prof.\, Aalborg University\, email cgraff@hst.aau.dk \nJonas L. Isaksen\, PhD\, University of Copenhagen \nJørgen Kanters\, Assoc. Prof.\, University of Copenhagen \nChristoffer Polcwiartek\, MD PhD\, Aalborg University \nStefan Sattler\, MD PhD\, Gentofte University Hospital \nLecturers:           Invited lecturers \n(see program here:  https://events.au.dk/electrocardiographybasicphysiologyepidemiologyandbedside/preliminary-program ) \nECTS:                2.0 \nDates:                21\, 22\, 23 and 24 May 2024 \nPlace:                 Feriecenter Slettestrand\, Slettestrandvej 140\, 9690 Fjerritslev \nDeadline:           17 April 2024 \nProgram:            BEN \nDescription: \nThis PhD course will enable you to dive into the world of advanced ECG research at Statistics Denmark. Explore arrhythmia models\, ion channels\, synthetic ECG generation\, machine learning\, antiarrhythmic drugs\, comparative ECGs syncope evaluation\, and more! \n Objectives of the PhD course aim to provide a comprehensive understanding of electrocardiography research across various domains\, encompassing both basic science and clinical applications.  \nTopics \n·        ECG research at Statistics Denmark \n·        Large and small animal ECG \n·        Cardiac ion channels \n·        Phenotypical ECG markers \n·        ECG in autoimmune and inflammatory diseases \n·        Processing and analyzing ECG data from ambulance and hospital sources \n·        Synthetic ECG generation \n·        Machine learning in ECG research \n·        Epidemiological ECG research \n·        Clinical EP studies \n·        Syncope \n·        Biventricular pacemaker therapy \nLearning objectives of the PhD course \nA PhD student who has met the objectives of the course will be able to: \n·        Understand the legal framework governing ECG research at Statistics Denmark\, including specific rules and regulations for accessing\, handling\, and             analyzing data \n·        Understand and critically evaluate the strengths and limitations of population-based ECG studies \n·        Understand the structure and format of ECG datasets at Statistics Denmark\, including the variables\, data types\, and metadata associated with each             record\, to facilitate effective data utilization and interpretation \n·        Explore collaborative research opportunities and interdisciplinary collaborations fostering partnerships with other research institutions to address                   complex research questions using ECG data \n·        Analyze and compare large and small animal models of arrhythmia to elucidate their relevance\, advantages\, and limitations in translational research             for human cardiac electrophysiology \n·        Explore cardiac ion channels\, action potentials\, and tissue level electrophysiology\, and apply this knowledge to design and interpret experimental                 studies \n·        Identify and characterize phenotypical ECG markers associated with various cardiac conditions\, including their sensitivity\, specificity\, and clinical                   significance for diagnosis and risk assessment \n·        Analyze the role of ECG in autoimmune and inflammatory diseases affecting the cardiovascular system\, including mechanisms of ECG alterations                 and their implications for disease monitoring \n·        Assess the ECG manifestations and drug effects in psychiatric diseases \n·        Design and implement algorithms for synthetic ECG generation\, considering physiological variations and noise characteristics \n·        Understand machine learning techniques to analyze ECG signals for pattern recognition\, risk stratification\, and predictive modeling in cardiac                         arrhythmias and related disorders \n·        Analyze the mechanisms of action\, pharmacokinetics\, and clinical efficacy of antiarrhythmic drugs\, including their indications\, contraindications\, and             adverse effects\, to optimize therapeutic strategies \n·        Examine the diagnostic utility of ECG in syncope evaluation\, including differentiating cardiac and noncardiac causes \n·        Evaluate the rationale\, patient selection criteria\, and clinical outcomes of biventricular pacemaker therapy in bundle branch block\, integrating principles of cardiac resynchronization therapy with ECG                     findings \n Program and practical details: Learn more about the PhD course here: https://dcacademy.dk/display/artikel/electrocardiography-basic-physiology-epidemiology-and-bedside \nRegistration: Please register 1) here on Moodle to obtain ECTS and 2) on the above website. \nEvaluation: Active participation \nImportant information concerning PhD courses: We have over some time experienced problems with no-shows for both project and general courses. It has now reached a point where we are forced to take action. Therefore\, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore\, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registration. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/electrocardiography-basic-physiology-epidemiology-and-bedside/
CATEGORIES:PhD Course
END:VEVENT
END:VCALENDAR