BEGIN:VCALENDAR VERSION:2.0 PRODID:-//DDSA - ECPv6.16.3//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:DDSA X-ORIGINAL-URL:https://ddsa.dk X-WR-CALDESC:Events for DDSA REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/Copenhagen BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20230326T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20231029T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20240331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20241027T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20251026T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20241202 DTEND;VALUE=DATE:20241205 DTSTAMP:20260615T123116 CREATED:20240424T104117Z LAST-MODIFIED:20240424T104117Z UID:10001127-1733097600-1733356799@ddsa.dk SUMMARY:Advanced control for building applications (2024) DESCRIPTION:Welcome to Advanced control for building applications \nOrganizer: Alireza Afshari \n Lecturers: Samira Rahnama\, Mahmood Khatibi \n ECTS: 3.0 (28 hours of work load per ECTS) \n Time: 2-4 December 2024 \n Place: Aalborg University \n Zip code: 9220 \n City: Aalborg \n Number of seats: 20 \n Deadline: 11 October 2024 \n Description:  \nTopic\, background and motivation for the course: \nDespite extensive research and successful implementation of advanced control techniques\, like MPC\, in other fields\, the application of such techniques is still limited in practice in building services engineering. One of the reasons seems to be the lack of knowledge among building service engineers about advanced control methods. There is a growing need for multidisciplinary education on advanced control methods in the built environment. \n Buildings use a large share of total energy use around 35–40% in many countries. In Denmark\, buildings account for 40% of the Danish energy use. Building energy-related activities are responsible for the 19% of GHG emissions worldwide. Therefore\, it is motivated to investigate the energy saving potential in the building sector. Advanced building control can considerably reduce building energy use. For instance\, numerous studies reported that advanced HVAC control can notably reduce energy use and mitigate GHG emissions with average energy savings of 13% to 28%. \nThe most popular advanced building control solution among the scientific community is Model Predictive Control (MPC) due its proven ability to handle constraints while optimizing the system performance. MPC on the supervisory level can be designed to find energy-efficient or cost-efficient control settings for the local controllers\, taking into account the system level characteristics\, interactions and comfort constraints. MPC combines building modelling\, measurement\, disturbance forecasting as well as information from external sources in the optimization formulation in order to find optimal control settings.\n   \nPrerequisites: \n–        Basic knowledge of a programming language (MATLAB/Python/R) \n–        Knowledge of basic control methods\, e.g. feedback control loop\, PID controllers \n–        Basic knowledge of building energy modelling and thermodynamics \n–        Basic knowledge of linear algebra \nLearning objectives: \nThis course is intended for PHD students in the built environment and building service engineers\, at national and international level\, who want to: \n–        increase their knowledge about the most recent advanced control techniques and their applications in the built environment \n–        learn the theory and practice of Model Predictive Control and MPC problem classes for building applications \n–        learn how to formulate an MPC problem for building applications \n–        learn how to implement a basic MPC algorithm in a small-scale experimental mock-up \nTeaching methods: \nTeaching method comprises of lecture presentations by the teachers\, simulation exercises with teachers’ supervision and discussion-based experimental demonstration possibly with competition between groups of student. The structure of the course is as follows: \n  \nDay 1 (Theoretical) \nLecture 1: A glimpse of control theory \n\nControl of dynamical systems: examples of control problems in building application\, types of control: model free (PID) and model-based control\, open loop\, and closed loop control)\nLTI system: eigen value and vector\, stability of the system\, controllability\nPole placement\nA MATLAB simulation example from building application for checking the controllability and stabilize the system with pole placement\n\nLecture 2: Optimal control design \n\nLQR control\nFull-state estimation: observability and Kalman filter design\nLQG control\nA MATLAB simulation example from building application for LQR\, Kalman filter and LQG control design\nLecture 3: Model Predictive Control\nGeneral concepts of MPC\nModelling (White box – Black box- Grey box)\nA MATLAB simulation example for Grey box modelling of a case study from a building application\n\nDay 2 (Simulation) \nLecture 1: An MPC design in MATLAB \n\nIntroduction to CVX toolbox\nCost functions formulization and constraints definition\nPresentation of a real-life case study (SmartVENT project) (Energy flexibility)\nSimulation exercise and homework (An example similar to SmartVENT project but simpler\, in which the white box model is either modelled in IDA ICE or be one of the SIMULINK models in MATLAB )\n\nDay 3 (Laboratory experiment) \nLecture 1: Introduction on the experimental setup \nExperimental exercise \n\nRun the experimental system and be familiar with the it\nInput-output data collection\nIdentify the system model and simulate the MPC controller\nImplement the MPC controller on the experimental setup\nComparison of a PI control and the MPC control on the system performance\n\nCriteria for assessment: \n–        Attendance in all course days as scheduled is required. \n–        Report on the simulation results. \n–        Presentation of the experimental results provided by each group \nKey literature: \n–        Jan Drgona et al.\, All you need to know about model predictive control for building\, Annual Reviews in Control\, 2020 \n–        Predictive control with constraints. by Maciejowski \n–        L. Wang\, Model Predictive Control System Design and Implementation Using MATLAB. Springer\, 2009 \n–        Henrik Madsen\, Statistical Modelling of Physical Systems (An introduction to Grey Box modelling) \n   \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/advanced-control-for-building-applications-2024/ LOCATION:Aalborg University TBA\, Aalborg CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20241125 DTEND;VALUE=DATE:20241130 DTSTAMP:20260615T123116 CREATED:20240424T102842Z LAST-MODIFIED:20240424T102842Z UID:10001126-1732492800-1732924799@ddsa.dk SUMMARY:Advanced Natural Language Processing for Industry: Theory and Practice (2024) DESCRIPTION:Welcome to Advanced Natural Language Processing for Industry: Theory and Practice\nOrganiser: Assistant Professor\, Chen Li\, cl@mp.aau.dk  \nLecturers:  \nAssociate Professor\, Dimitris Chrysostomou\, Department of Materials and Production \nAssociate Professor\, Thomas Ditlev Brunø\, Department of Materials and Production \nAssociate Professor\, Elizabeth Jochum\,  Department of Communication and Psychology \nAssociate Professor\, Simon Bøgh\, Department of Materials and Production \nAssistant Professor\, Chen Li\, Department of Materials and Production \nECTS: 3 \nTime: 25th – 29th November 2024 \nPlace: Aalborg University (please state venue: Aalborg) \nDeadline: 4 November 2024 \nMax no. of participants: 20 \nDescription:  \nThe aim of this course is to equip PhD students with a comprehensive understanding of cutting-edge natural language processing (NLP) technologies and their application in real-world industrial settings.  Solid understanding of these vital NLP techniques equips business and engineering PhD students with the necessary skills to leverage textual data effectively to solve the real-world problems\, e.g.\, improving customer satisfaction\, developing intelligent systems\, and making data-driven decisions. This leads to success in today’s data-driven and language-driven industrial landscape. The topics covered here are text classification\, named entity recognition\, dialogue systems and applications of large language model (e.g. ChatGPT).  In addition to theoretical knowledge\, this course places a strong emphasis on practical implementation and problem-solving. Participants will engage in hands-on exercises\, working with industry-standard tools and libraries. Participants will develop the ability to identify and solve NLP challenges specific to their respective fields. By the end of the course\, participants will possess the skills necessary to navigate the rapidly evolving landscape of NLP technologies. They will have the ability to develop and deploy advanced NLP models to tackle complex language-related problems faced by businesses and industries.   \nTarget Students: \nThe course will target PhD students in the fields of business and engineering from all the Danish universities and ideally from the universities all over the world. Industry professionals are also encouraged to participate. Maximum 20 students will be selected to attend the course. \n  \nCourse Language: \nThe course will be given in English. \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/advanced-natural-language-processing-for-industry-theory-and-practice-2024/ LOCATION:Aalborg University TBA\, Aalborg CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20241113 DTEND;VALUE=DATE:20241121 DTSTAMP:20260615T123116 CREATED:20240424T102907Z LAST-MODIFIED:20240424T102907Z UID:10001105-1731456000-1732147199@ddsa.dk SUMMARY:Introduction to multivariate data analysis (2024) DESCRIPTION:Description: \nModern laboratory equipment produces huge amounts of experimental data — spectral vectors with hundreds of wavelengths\, microarrays\, gene expression data\, sensors\, multi-channel images and many others. Even conventional measurements may end up with tens to hundreds of variables. Such data represent a wealth of potential information but usually only a part of it relates to a problem of interest. \nThis course teaches how to extract problem-dependent information from multivariate data. The practical part of the course assuming using R for calculations and visualization of results. \nThe course is split in to two parts. The first part (3 days\, 2 ECTS) introduces descriptive and inferential statistics\, as well as data exploration with Principal Component Analysis. The second part (3 days\, 2 ECTS) is mainly devoted to supervised analysis of multivariate data\, including regression and validation\, preprocessing and variable selection as well as classification. \nIn each part lectures are supplemented with a suite of real life examples and exercises as well as assignments\, with which students will try the discussed methods by solving various data analysis problems. To complete the course\, participants have to work on three mini-projects and submit their results in form of reports within 1 month after the main part if finished. \n\n\n\n\nOrganizer: \n\n\nAssociate Professor Sergey Kucheryavskiy\, E-mail svk@bio.aau.dk \n\n\n\n\nLecturers: \n\n\nAssociate Professor Sergey Kucheryavskiy \n\n\n\n\nECTS: \n\n\n5 (2\,5 + 2\,5) \n\n\n\n\nDate: \n\n\nNovember 13-15 (1st part)\, 18-20 (2nd part)\, 2024 \n\n\n\n\nPlace: \n\n\nSection for Chemistry and Chemical Engineering \nDepartment of Chemistry and Bioscience \nAalborg University\, campus Esbjerg\n Niels Bohrs vej\, 8 \n6700\, Esbjerg \nDenmark \n\n\n\n\nDeadline: \n\n\n10.10.2024 \n\n\n\n\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/introduction-to-multivariate-data-analysis-2024/ LOCATION:Aalborg University TBA\, Aalborg CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20241106 DTEND;VALUE=DATE:20241114 DTSTAMP:20260615T123116 CREATED:20240424T103011Z LAST-MODIFIED:20240424T103011Z UID:10001081-1730851200-1731542399@ddsa.dk SUMMARY:Bayesian Statistics\, Simulation and Software (2024) DESCRIPTION:Description: During the last decades\, Bayesian statistics has gained enormous popularity as an elegant and powerful computational tool to perform statistical analysis in complex stochastic models as applied in engineering\, science and medicine. Bayesian statistics offers an alternative approach to traditional data analysis by including prior knowledge about the model parameters in form of a prior distribution. Using Bayes formula the prior distribution is updated from the posterior distribution by incorporating the observed data by means of the likelihood. Subsequently statistical inference about the unknown model parameters is derived from the posterior distribution. However\, the posterior distribution is often intractable due to high-dimensional complex integrals implying that approximate stochastic simulation techniques such as Markov Chain Monte Carlo (MCMC) methods become crucial. This course reviews the basics ideas behind Bayesian statistics and Markov chain Monte Carlo (MCMC) methods. Background on Markov chains will be provided and subjects such as Metropolis and Metropolis-Hastings algorithms\, Gibbs sampling and output analysis will be discussed. Furthermore\, graphical models will be introduced as a convenient tool to model complex dependency structures within a stochastic model. The theory will be demonstrated through different examples of applications and exercises\, partly based on the software package R. \nPrerequisites: Note that this will not be a “a black box approach” to the subject as there will be some mathematical abstraction which is needed in order to construct meaningful Bayesian models and simulation procedures. In principle the course is accessible to those new to these subjects\, however\, some mathematical training will be an advantage and a basic knowledge of statistics and probability theory as obtained through engineering studies at Aalborg University is definitely expected. \nAdditional information and assessment: All course material and additional information is available at the course website https://asta.math.aau.dk/course/bayes/2024/. In particular note the assessment of the course through active participation and a hand-in exercise. \nFrequently asked questions: \nQ: If I participate in the course\, can you then help me analyze a dataset that I work with as part of my ph.d. project. \nA: No\, I am afraid that this is not possible \nQ: I would like to participate in the course\, but during a part of the course period I can not be present. Is it possible to follow to course via Skype or similar? \nA: Maybe\, to some extend. See the course website \nQ: I am not a ph.d. student\, but I would like to participate in the course anyway. Is that possible? \nA: You will have to ask the doctoral school: aauphd@adm.aau.dk \nQ: I realize that I am late for enrollment\, but I would really like to participate. Is it possible. \nA: You will have to ask the doctoral school: aauphd@adm.aau.dk \n  \nOrganizer: Professor Jesper Møller – jm@math.aau.dk \n Lecturers: Professor Jesper Møller – jm@math.aau.dk; Associate Professor Ege Rubak – rubak@math.aau.dk \nECTS: 4.0 \n Time:  06\, 07\, 08 and 11\, 12\, 13 November 2024 \n Place: TBA \n Zip code: 9220 \n City: Aalborg \n Number of seats: 40 \n Deadline: 16 October 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/bayesian-statistics-simulation-and-software-2024/ LOCATION:Aalborg University TBA\, Aalborg CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20241009 DTEND;VALUE=DATE:20241025 DTSTAMP:20260615T123116 CREATED:20240424T090727Z LAST-MODIFIED:20240424T090727Z UID:10001080-1728432000-1729814399@ddsa.dk SUMMARY:AI for the people (2024) DESCRIPTION:Description: The notion of Artificial Intelligence (AI) dates back approx. 70 years as a research field and even longer if one considers fiction writers. A number of different definitions of AI has been suggested over the years\, but none seem to capture what AI is. This might be due to the fact that AI is about computer algorithms that behave intelligently. And since the capabilities of computer algorithms improve over time\, no static definition is possible. \nOne aspect of AI is the ability to learn or adapt dynamically. This concept has inspired numerous Sci-fi books and movies with the underlying theme of man vs AI (often manifested in a robot). From this follows naturally ethical and regulatory considerations. But until recently\, such considerations (see for example the three Robotic laws defined by the sci-fi writer I. Asimov) have been speculative since current AI algorithms (and their manifestation in mechanical devices) have performed poorly and hence never left university labs around the world. Recently\, however\, fast hardware and massive amount of data have allowed revisiting one particular AI algorithm invented in the 80s\, namely Artificial Neural Networks (ANN)\, and increasing the size of the networks used in these models. This was exemplified via image processing for recognizing hand-written digits and resulted in amazing results. Inspired by this success ANN (now known as Deep Learning (DL)) was quickly picked up by other research fields where similar successes have been witnessed. \nDL algorithms can now outperform humans on a number of tasks. Moreover\, they can\, to a certain degree\, learn new tasks. An important point in this regard is that the algorithm is so complex that it is next to impossible to understand its inner workings. So\, we seem to be facing a reality where AI\, in a not too distant future\, will be used to make decisions (simply because it is of better than humans). This raises a number of ethical and regulative questions such as\, for instance\, 1) how we ensure that AI systems are not discriminating against certain groups in the population\, 2) how do we ensure transparency about the decisions made by AI systems\, and relatedly 3) could and should individuals be given a substantial right to an explanation of decisions made by such systems and a substantial right not to be subjected to automated decision-making (GDPR). Since many of the currently developed AI systems operate on the basis of large amounts of data\, the development and use of such systems also reinvigorate the ethical issues related to ‘Big data’. Finally\, there are problems related to the efficacy and safety of AI systems. This raises questions not only of how appropriate monitoring of the development of these systems can be secured\, but also and more importantly about the appropriate domains for use. \nThese questions and related questions are the core focus of the PhD course on ‘AI for the people’. The aim is to raise an awareness in the participants. To this end the course will be a combination of lectures\, debates and an assignment\, and includes the following topics: \n\n\n\nIntroduction to AI\nEthical issues in the development and use of AI\nIndustry perspective on AI\n\n\n\nOrganizers: Professor Thomas B. Moeslund\, tbm@create.aau.dk  \n Lecturers: Professor Thomas B. Moeslund\, tbm@create.aau.dk  \n ECTS: 2.0 \n Time: 09\, 10 and 24 October 2024 \n Place: Aalborg University  \nZip code: 9220 \nCity: Aalborg \nNumber of seats: 30 \n Deadline: 18 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/ai-for-the-people-2024/ LOCATION:Aalborg University TBA\, Aalborg CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20241007 DTEND;VALUE=DATE:20241010 DTSTAMP:20260615T123116 CREATED:20240424T090852Z LAST-MODIFIED:20240424T090852Z UID:10001116-1728259200-1728518399@ddsa.dk SUMMARY:Applications of AI to Modern Data Management Systems (2024) DESCRIPTION:Welcome to Applications of AI to Modern Data Management Systems \nOrganizer: Professor Torben Bach Pedersen \nLecturers: Professor Wolfgang Lehner \nECTS: 2 \nDate/Time: 7-9 October 2024 \nDeadline: 16 September 2024 \nMax no. Of participants: 15 \nDescription: \nThe course will cover applications of AI to modern data management systems\, including learned indexes\, learned optimizers\, and other state of the art applications of modern AI to design of data management system or direct replacement of their components. The course will cover several types of systems\, e.g.\, both relational database management systems and other data management/data processing systems. \nLearning objectives: \nThe objective of the course is to provide students with a working understanding of how AI can optimize the internal workings of modern data management systems and how they can apply AI in their own data management system research. \nPrerequisites: \nBachelor and master degrees in computer science or software engineering\, including knowledge on machine learning and data management as introduced in typical undergraduate courses. \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/applications-of-ai-to-modern-data-management-systems-2024/ LOCATION:Aalborg University TBA\, Aalborg CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20241002 DTEND;VALUE=DATE:20241005 DTSTAMP:20260615T123116 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:20240924 DTEND;VALUE=DATE:20241024 DTSTAMP:20260615T123116 CREATED:20240424T091017Z LAST-MODIFIED:20240424T091017Z UID:10001079-1727136000-1729727999@ddsa.dk SUMMARY:Signal and Spectral Analysis: extracting information from noisy data (2024) DESCRIPTION:Description:  \nIn many situations\, a number of observations are made which contain some information about an underlying phenomenon we are interested in. Examples of this are: \n\nThe diagnosis of the Parkinson’s disease from a telephone recording\,\nThe assessment of bearing wear from vibrational data\,\nThe automatic transcription of music\,\nOrder tracking analysis of rotating machines\,\nAutomated analysis of the heart sound\, and\nHarmonic analysis in power systems.\n\n  \nTo solve these and many other problems\, a signal analysis toolbox is needed. This course focuses ondeveloping\, explaining\, understanding\, and using such tools. Specifically\, the course covers important and general concepts such as:​ \n\nSignal modelling: Which models exist and what are their applicability and limitations?\nSpectral analysis: Why is signal analysis often performed as a function of frequency and how do you do it?\nInference and parameter estimation: How do you estimate model parameters accurately and quantify how well you do?\n\nThe course is primarily developed for doctoral students from medicine and various engineering and natural science disciplines who wish to not only apply\, but also to understand signal and spectral analysis. Consequently\, the course is rooted in a principled and systematic exposition of fundamental concepts and tools and in a scientific approach which promotes the creation of knowledge over improving state-of-the-art by ε percent. An important goal of the course is to make doctoral students able to solve a signal and spectral analysis task based on data from their own Ph.D.-project. This is integrated in the course via a mini project. \nKeywords: Filtering\, statistical signal processing\, estimation theory\, maximum likelihood\, powerspectral density estimation\, modelling\, least squares\, autoregressive\, nonnegative matrix factorizations\, sparsity\, periodic signals\, Fourier analysis\, line spectra. \nPrerequisites: Basic probability theory\, linear algebra\, signal processing\, and experience with MATLAB and/or Python programming. \nOrganizer: Assoc. Professor Jesper Rindom Jensen – jrj@es.aau.dk \n Lecturers: Assoc. Professor Jesper Rindom Jensen – jrj@es.aau.dk \n ECTS: 3.0 \n Time: 24 September\, 01\, 08\, 15 and 23 October 2024 \n Place: Aalborg University  \nZip code: 9220 \n City: Aalborg \nTeams Channel: LINK \nNumber of seats: 30 \nDeadline: 03 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/signal-and-spectral-analysis-extracting-information-from-noisy-data-2024/ LOCATION:Aalborg University TBA\, Aalborg CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240923 DTEND;VALUE=DATE:20240928 DTSTAMP:20260615T123116 CREATED:20240424T091025Z LAST-MODIFIED:20240424T123137Z UID:10001078-1727049600-1727481599@ddsa.dk SUMMARY:Mixed Models with Biomedical and Engineering Applications (2024) DESCRIPTION:Description: Mixed models provide a flexible framework for analyzing data with multiple sources of random variation and they are indispensable in many medical\, biological\, and engineering applications. When treatments are tested in medical applications\, the responses for individuals receiving the same treatment often vary due to unobserved genetic factors and this variation must be taken into account when comparing ifferent treatments. Similarly\, in agricultural field trials\, random soil variation affects the yield within plots. In quality control applications\, the variability of the output of a production process may\, apart from random noise\, e.g. depend on the batches of raw material used and the employee involved in the manufacturing process.  \n The course will provide an introduction to statistical analysis with linear mixed models. Linear mixed models is a unified framework for classical random effects ANOVA models\, random coefficient models and linear models for longitudinal data with associated user-friendly implementations in R and SPSS. Linear mixed models moreover provide generalizations of the classical models to complex data not covered by he standard statistical toolbox.  \n The course will focus on modeling with mixed models\, on how a statistical analysis can be carried out for a mixed model\, and on interpretation of models and results. Hands-on experience with real data will be obtained through computer exercises. \n Prerequisites: A basic knowledge of statistics (linear regression) and probability theory (random variables\, expectation variance and covariance) is expected. \n Organizer: Professor Rasmus Waagepetersen – rw@math.aau.dk \n Lecturers: Professor Rasmus Waagepetersen – rw@math.aau.dk \n ECTS: 1.5 \n Time: 23 and 27 September 2024 \n Place: Aalborg University \n Number of seats: 20 \n Deadline: 02 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/mixed-models-with-biomedical-and-engineering-applications-2024/ LOCATION:Aalborg University TBA\, Aalborg CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240916 DTEND;VALUE=DATE:20241008 DTSTAMP:20260615T123116 CREATED:20240424T091143Z LAST-MODIFIED:20240424T091143Z UID:10001082-1726444800-1728345599@ddsa.dk SUMMARY:Tools for Scientific Software Development and Data Science (2024) DESCRIPTION:Welcome to Tools for Scientific Software Development and Data Science (2024) \nThe development of eScience and Data Science across research fields means many researchers have to spend a significant amount of time at their computers. As a consequence\, we need to ensure that our skill set and toolbox is up to date and that we can accurately\, effectively and in a research-wise justifiable manner conduct our research with a computer. \n\n  \nWho is this course for? \nIf you in your daily work do any of these: \n\nProcess data on a computer\nAdapt code and scripts from colleagues or peers\nWrite code/scripts used by you\, your colleagues or peers\n\nthen this course is for you. However\, this is not a programming course. \nAre you looking for a course on a specific programming language? \nCheck out the following courses: \n\nScientific Computing using Python – 1. Python + Scientific Computing\nScientific Computing using Python – High Performance Computing In Python\nData Science Using R\n\nObjectives \nIn this course you will learn the practical skills and craftsmanship to increase your day-to-day research productivity and be able to use and/or produce scientific software with a high degree of compliance to modern research standards. After the completion of the course you should \n\nhave knowledge and understanding of collaboration practices in writing program code and managing data to ensure high quality scientific research and development\nbe able to with confidence assess and utilise IT work environments for scientific research and development projects\nbe able to apply current software development principles in development of program code and computational scripts for the use in scientific research\n\nIn more common terms you will learn to e.g. \n\napply the widely used commandline interface/shell bash in your daily work.\napply the widely used version control system Git in your daily work.\nunderstand concepts related to computational reproducibility and data management.\n\nFormat \nHands-on interactive three-day event with participatory live-coding\, demos and presentations. The participants are encouraged to follow and run the same examples as shown during the course. The workshop will contain several smaller practical 5-10 minutes exercises and breaks. \nCourse structure \n1. Day: \n\n\n\nIntroduction: why are we here?\nWork in practice: what IT resources are available to me?\nGet efficient with the command line interface (shells: MacOS(zsh)\, Linux(bash) – Windows users will use a Linux environment)\nBasic version control: Git and what you need for your everyday work\n\n\n\n2. Day: \n\n\n\nCode smart: software development principles to live by\nBe smart: using automatic testing (with examples in Matlab\, R and Python)\nWork smart: instead of (re)writing code use scripts to combine existing rutines to produce the output you need\n\n\n\n\n\n3. Day: \n\n\n\nAdvanced version control: Git as a platform for collaboration\nShow off your examples with Jupyter notebook\nGet more out of your code: Computational Reproducibility\nGet more out of your data: FAIR (findability\, accessibility\, interoperability\, and reusability).\n\n\n\nWe will not teach a specific programming language and will try to keep the presented material as language-independent as possible. \nThis is not a programming course. \nPrerequisites \n\nYou will need to bring a laptop with Windows / OS X / Linux.\nYou know the basics of a least one programming language.\nYou can navigate your computer\, locate files etc.\nRead Wilson et. al. “Good enough practices in scientific computing” and start thinking about the presented ideas and to what extent it can be adapted in your work.\n\nWe expect that: \n\nYou actively participate and work on the examples and exercises.\nYou talk to your neighbors and help each other.\nAsk for help if both you and your neighbors are stuck.\n\nCourse project \nThe course project will contain several elements from the course. Participants are presented with a default project\, or can take on a project based on their existing work if they find this option suitable. The project will require additional work following the three course days. \nECTS: 2 \nParticipants attending at least 80% of the course and submitting an acceptable course project receive credits. \nLecturers \nSpecial consultant Gergely István Barsi \n\nDates: 16\, 17 September and 7 October 2024 \nLocation: TBA \nCity: Aalborg \nZip Code: 9220 \nNumber of seats: 30 \nDeadline: 26 August 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/tools-for-scientific-software-development-and-data-science-2024/ LOCATION:Aalborg University TBA\, Aalborg CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240624 DTEND;VALUE=DATE:20240627 DTSTAMP:20260615T123116 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:20240602 DTEND;VALUE=DATE:20240629 DTSTAMP:20260615T123116 CREATED:20240424T090353Z LAST-MODIFIED:20240424T090353Z UID:10001117-1717286400-1719619199@ddsa.dk SUMMARY:Welcome to Big Data Integration DESCRIPTION:Welcome to Big Data Integration \nOrganizer: Matteo Lissandrini\, Katja Hose \nLecturers: Giovanni Simonini\, University of Modena and Reggio Emilia (Italy) \nECTS: 2 \nDate/Time: June 2024 \nDeadline: 10 May 2024 \nMax no. Of participants: 20 \nDescription: The course aims at illustrating recent advancements in the field of big data integration from both the practical and methodological perspective. In particular\, the focus will be on tools and techniques for large and heterogenous datasets\, such as data lakes and open data. The main tackled topics will be: (i) Data discovery; (ii) Entity Resolution\, i.e.\, the task of identifying and integrating records that refer to the same real-world entity in different datasets when an explicit identifier is not provided; (iii) data preparation\, i.e.\, the set of preprocessing operations performed to transform the data at the structural and syntactical level. \nPrerequisites:  Familiarity with a programming language. \nLearning objectives: Students will learn core techniques and technologies for the tasks of (i) Data discovery; (ii) Entity Resolution; (iii) data preparation. \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/welcome-to-big-data-integration/ LOCATION:Aalborg University TBA\, Aalborg CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240527 DTEND;VALUE=DATE:20240530 DTSTAMP:20260615T123116 CREATED:20240424T090448Z LAST-MODIFIED:20240424T090448Z UID:10001119-1716768000-1717027199@ddsa.dk SUMMARY:Biophysical Expression\, Affect & Movement (BEAM) (2024) DESCRIPTION:Welcome to Biophysical Expression\, Affect & Movement (BEAM) \nOrganizer: Daniel Overholt \nLecturers: Daniel Overholt (US/DK)\, Elizabeth Jochum (US/DK)\, Mark-David Hosale (US/CA)\,  Alan Macy (US)\, Grisha Coleman (US)\, and Marco Donnarumma (DE/IT). \nECTS: 3 \nDate/Time: 27\, 28\, 29 May 2024 \nDeadline: 06 May 2024 \nMax no. of participants: 20 \nEnrolment: Sign up through the link  \nDescription: Biophysical Expression\, Affect & Movement (BEAM) introduces students to cutting edge research trends and technology platforms that monitor and augment human performance across both the creative industries and health sectors.  The course offers hands-on workshop activities using advanced sensor technologies for physiological data\, applied to real-time performance and augmented human capabilities with computation\, including machine learning. \nOne platform to be explored is the BioMECI with guest lecturers / developers Mark-David Hosale and Alan Macy. Design and integration with other platforms will also be explored\, for example Bio-X sensors integrating machine-learning approaches to multimodal human-computer interaction. The course will also include a discussion AI Ethics and Prosthetics. This course has cross-over appeal for creative computing applications (audiovisual interaction\, sound and music computing) and health/rehabilitation applications (tele-health\, digital health solutions/monitoring). Programming experience\, Design of HCI systems\, Interest in affective computing and real-time systems exploring the arts\, such as music / dance & movement / visual forms of expression and/or interest in working with medical devices/sensing/health monitoring devices for training and rehabilitation. \nThe course will be hosted by the Sound & Music Computing research group and take place in both the Augmented Performance Lab  and the  Manufakturet labs. The course will be supported by reserarchers from the RELATE Research Laboratory for Art and Technology. \nPrerequisites: Programming experience\, Design of HCI systems\, Interest in affective computing and real-time systems exploring the arts\, such as music / dance & movement / visual forms of expression and/or interest in working with medical devices/sensing/health monitoring devices for training and rehabilitation. \nRequired Reading \nDonnarumma\, M. (2017). On Biophysical Music. In: Miranda\, E. (eds) Guide to Unconventional Computing for Music. Springer\, Cham. https://doi.org/10.1007/978-3-319-49881-2_3 (pdf) \nTanaka\, A. (2019). Embodied Musical Interaction. In: Holland\, S.\, Mudd\, T.\, Wilkie-McKenna\, K.\, McPherson\, A.\, Wanderley\, M. (eds) New Directions in Music and Human-Computer Interaction. Springer Series on Cultural Computing. Springer\, Cham. https://doi.org/10.1007/978-3-319-92069-6_9 (pdf) \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/biophysical-expression-affect-movement-beam-2024/ LOCATION:Aalborg University TBA\, Aalborg CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240514 DTEND;VALUE=DATE:20240516 DTSTAMP:20260615T123116 CREATED:20240424T082330Z LAST-MODIFIED:20240424T082330Z UID:10001113-1715644800-1715817599@ddsa.dk SUMMARY:Principles of Data Visualisation and storytelling (2024) DESCRIPTION:Welcome to Principles of Data Visualisation and storytelling \nOrganizer: Gabriela Montoya \nLecturers: Luis-Daniel Ibáñez (Lecturer at University of Southampton\, UK) \nECTS: 2 \nDate/Time: 14-15 May 2024 \nDeadline: 23 April 2024 \nMax no. Of participants: 25 \nDescription: For most tasks where we collect and/or analyse data\, the ability to visualise what we are doing is critical for making sense to yourself and your collaborators. Appropriate visual and narrative support of the results of the analysis is even more important for communicating (and convincing!) other stakeholders such as grant decision makers and potential investors.        \nIn this course we will go beyond how to use a library to generate a chart and learn how to choose the appropriate chart depending on what we want to highlight and how to structure our visuals to create a compelling data story.  To do so\, we will delve into the principles of human perception and study the narrative patterns that we can apply to tell a story with data.  \nThe assessment of the course includes an individual design and implementation of a short data story (3-5 screens) and short report (2 pages) justifying the narrative pattern and chart choices. \nKnowledge will be put into practice by designing and developing a short data story in a theme of your choice. \nPrerequisites: Previous experience with a data analysis software (R\, MATLAB\, any Python-based\, Excel) \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/principles-of-data-visualisation-and-storytelling-2024/ LOCATION:Aalborg University TBA\, Aalborg CATEGORIES:PhD Course END:VEVENT END:VCALENDAR