BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//DDSA - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
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
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20270328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20271031T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260323
DTEND;VALUE=DATE:20260328
DTSTAMP:20260414T212142
CREATED:20260119T120311Z
LAST-MODIFIED:20260216T150913Z
UID:10001864-1774224000-1774655999@ddsa.dk
SUMMARY:Reinforcement Learning
DESCRIPTION:Welcome to Reinforcement Learning \nDescription: \nAn intelligent system is expected to generate policies autonomously to achieve a goal\, which is mostly to maximize a given reward function. Reinforcement learning is a set of methods in machine learning that can produce such policies. To learn optimal actions in an environment that is not fully comprehensible to itself\, an intelligent system can use reinforcement algorithms to leverage its experience to figure out optimal policies. Nowadays\, reinforcement learning techniques are successfully applied in various engineering fields\, including robotics (DeepMind’s walking robot) and computer-games (AlphaGo and TD-Gammon). \nDeveloped independently from reinforcement learning\, dynamic programming and related stochastic optimisation is a set of algorithms that generate policies assuming that the environment is fully comprehensible to the intelligent system. Therefore\, dynamic programming provides an essential base to learn reinforcement learning. The course aims at building a fundamental understanding of both methods based on their relations to each other and on their applications to similar problems. \nThe course consists of the following topics: \nMarkov decision processes\, dynamic programming for infinite time and stopping time\, reinforcement learning\, safe learning and verification tools for reinforcement learning. \nPrerequisites: Basic knowledge of mathematics: calculus and probability \nLearning objectives: \n– General Introduction to machine learning herein Reinforcement Learning \n– Markov Decision Processes and Dynamic Programming \n– Reinforcement Learning with Temporal-Difference Learning \n– Policy prediction with approximation \n– Verification tool UPPAAL for model-based reinforcement learning \nKey literature: Richard S. Sutton\, Andrew G. Barto\, Reinforcement Learning \nFor additional information\, updates\, and registration\, please refer to AAU PhDMoodle via the link provided on the right side of this page. \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/reinforcement-learning/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260311
DTEND;VALUE=DATE:20260313
DTSTAMP:20260414T212142
CREATED:20260115T145202Z
LAST-MODIFIED:20260216T150733Z
UID:10001839-1773187200-1773359999@ddsa.dk
SUMMARY:Introduction to Probabilistic Machine Learning
DESCRIPTION:Welcome to Introduction to Probabilistic Machine Learning \nDescription: \nMachine learning (ML) and artificial intelligence have had major impacts on all areas of society and across research disciplines. Probabilistic ML provides a principled approach\, based on probabilistic methods\, to develop intelligent systems that make optimal decisions under uncertainty. Many problems in science can be casted as decision problems under uncertainty. In consequence\, Probabilistic ML allows the application of powerful ML techniques to a wide range of problems in many relevant fields. Knowledge and experience with these types of techniques are therefore important\, not only for researchers in machine learning and computer science\, but also for researchers across disciplines as made evident by the recently established AI for the people centre at AAU. \nFor additional information\, updates\, and registration\, please refer to AAU PhDMoodle via the link provided on the right side of this page. \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/introduction-to-probabilistic-machine-learning/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260310
DTEND;VALUE=DATE:20260313
DTSTAMP:20260414T212142
CREATED:20260115T145147Z
LAST-MODIFIED:20260115T145147Z
UID:10001714-1773100800-1773359999@ddsa.dk
SUMMARY:Methods of AI in Modern Data Management Systems (2025)
DESCRIPTION:Welcome Methods of AI in Modern Data Management Systems \nDescription: \nThis course is at the intersection of AI and data system design. The basic idea is to learn where the application (!) of AI methods may benefit the design of specific components of a data management system. The students will learn about opportunities as well as limitations of AI when designing a large software system. The course will also offer a hands-on experience section\, which underpins the conceptual discussion with real use cases. \nPrerequisites: \nA background in computer science is assumed. In particular\, the participants should have knowledge about and experience with relational database management systems. Further\, the participants should have programming experience (ideally Python). \nFor additional information\, updates\, and registration\, please refer to AAU PhDMoodle via the link provided on the right side of this page. \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/methods-of-ai-in-modern-data-management-systems-2025/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260223
DTEND;VALUE=DATE:20260313
DTSTAMP:20260414T212142
CREATED:20260115T113304Z
LAST-MODIFIED:20260115T113310Z
UID:10001848-1771804800-1773359999@ddsa.dk
SUMMARY:Methods of AI in Modern Data Management Systems (2026)
DESCRIPTION:Welcome Methods of AI in Modern Data Management Systems \nDescription: \nThis course is at the intersection of AI and data system design. The basic idea is to learn where the application (!) of AI methods may benefit the design of specific components of a data management system. The students will learn about opportunities as well as limitations of AI when designing a large software system. The course will also offer a hands-on experience section\, which underpins the conceptual discussion with real use cases. \nPrerequisites: \nA background in computer science is assumed. In particular\, the participants should have knowledge about and experience with relational database management systems. Further\, the participants should have programming experience (ideally Python). \nFor additional information\, updates\, and registration\, please refer to AAU PhDMoodle via the link provided on the right side of this page. \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/methods-of-ai-in-modern-data-management-systems-2026/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251027
DTEND;VALUE=DATE:20251030
DTSTAMP:20260414T212142
CREATED:20250312T113901Z
LAST-MODIFIED:20250312T113901Z
UID:10001516-1761523200-1761782399@ddsa.dk
SUMMARY:Human Centered Artificial Intelligence
DESCRIPTION:Description:  \nArtificial Intelligence has experienced a tremendous increase in attention in recent years across all sectors\, including health\, transportation\, finance\, construction\, and entertainment. In the realm of software engineering\, integrating AI in a human-centered manner is crucial for ensuring that these technologies enhance human capabilities and maintain ethical integrity. Ben Shneiderman envisions “computing devices that dramatically amplify human abilities\, empowering people and ensuring human control.” He proposes that “Human-Centered AI (HCAI) enables people to see\, think\, create\, and act in extraordinary ways\, by combining potent user experiences with embedded AI support services that users want.” \nThis three-day PhD course\, inspired by the Copenhagen Manifesto\, explores the intersection of AI and software engineering\, focusing on human-centric design principles and ethical considerations. Participants will focus into the core values of human-centered AI\, such as responsibility\, ethics\, transparency\, equity\, inclusivity\, continuous learning\, and environmental sustainability. \nThe course is structured to provide an interactive and comprehensive learning experience\, utilizing Liberating Structures to facilitate active engagement and collaboration. Participants will: \nUnderstand the foundational principles of human-centered AI and their application in software engineering. \nExplore strategies for designing AI systems that prioritize human needs\, transparency\, and equity. \nDevelop practical skills in implementing and evaluating human-centered AI technologies. \nReflect on the ethical implications and societal impact of AI in software engineering. \nBy the end of the course\, participants will have a robust understanding of how to integrate human-centered principles into their work\, ensuring that AI technologies serve the common good and promote human wellbeing. \nLearning objectives: Understand Foundational Principles of Human-Centered AI: Grasp the core concepts and principles of human-centered AI\, including responsibility\, ethics\, transparency\, and equity. \nComprehend how these principles are applied in the context of software engineering and AI development. \nDevelop Skills in Human-Centered AI Design: \nLearn and apply strategies for designing AI systems that prioritize human needs\, autonomy\, and user experience. \nGain practical skills in implementing and evaluating AI technologies that enhance human capabilities while ensuring ethical integrity. \nEvaluate Ethical Implications of AI: \nCritically assess the ethical challenges and societal impacts associated with AI technologies. \nDevelop frameworks and guidelines for ethical AI development\, with a focus on minimizing harm and promoting fairness and inclusivity. \nPromote Transparency and Equity in AI Systems: \nUnderstand the importance of transparency in AI development and learn methods for implementing it effectively. \nExplore strategies to ensure equity in AI systems\, particularly in terms of access\, fairness\, and bias mitigation. \nFoster Inclusivity and Continuous Learning in AI Projects: \nCultivate an understanding of the importance of inclusivity and continuous learning in AI development teams. \nDevelop strategies to support diverse and inclusive environments within AI projects and organizations. \nIncorporate Environmental Sustainability into AI Development: \nIdentify practices that hinder environmental sustainability in AI development. \nLearn and apply strategies to reduce the environmental impact of AI technologies\, ensuring sustainable innovation. \nEngage in Collaborative Learning and Knowledge Sharing: \nParticipate in interactive workshops using Liberating Structures to enhance collaboration and idea generation. \nContribute to group discussions and activities that reinforce the application of human-centered AI principles in real-world scenarios. \nOrganizer: Daniel Russo \nLecturers: Daniel Russo \nECTS: 3 ECTS \nTime: 27\, 28\, 29 October 2025 \nPlace: Aalborg University \nZip code: 9220 \nCity: Aalborg \nMaximal number of participants: 20 \nDeadline: 6 October 2025 \nImportant information concerning PhD courses:  \nThere is 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 the 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 of the course. \nWe cannot ensure any seats before the deadline for enrolment\, all participants will be informed after the deadline\, approximately 3 weeks before the start of the course. \nTo attend courses at the Doctoral School in Medicine\, Biomedical Science and Technology you must be enrolled as a PhD student. \nFor inquiries regarding registration\, cancellation or waiting list\, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you. \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/human-centered-artificial-intelligence/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250916
DTEND;VALUE=DATE:20250919
DTSTAMP:20260414T212142
CREATED:20250312T113304Z
LAST-MODIFIED:20250312T113304Z
UID:10001483-1757980800-1758239999@ddsa.dk
SUMMARY:Design and Query Processing in Specialized Data Management Systems
DESCRIPTION:Welcome to Design and Query Processing in Specialized Data Management Systems \nDescription: Relational Database Management Systems have been applied in many different domains and build the foundation of every larger software application stack. However\, highly specific applications and use cases often require more tailored systems along with a non-relational data model and use-case specific query language. Within this course\, we will identify such use-cases\, discuss the requirements\, and investigate typical architecture properties and query capabilities of four different types of systems: key-values stores with eventual consistency\, document stores for JSON data\, property graph database systems\, and timeseries data management systems. \nPrerequisites: A background in computer science is assumed. In particular\, the participants should have knowledge about and experience with relational database management systems. Further\, the participants should have programming experience (Java/Python). \nLearning objectives: The objective of the course is to provide students with a comprehensive understanding of the key characteristics of workloads and data models that favor specialized data management systems. Students will gain hands-on experience with four different use-cases demanding different types of data management systems. The goal is to equip students with the knowledge to effectively leverage specialized data management systems (e.g. time series systems) in their own research activities. \nOrganizer: Wolfgang Lehner \nLecturers: Wolfgang Lehner \nECTS: 2 ECTS \nTime: September 16 – 18\, 2025\, from 9-17 all days \nPlace: Room 02.90\, Selma Lagerløfsvej 300\, Aalborg University \nZip code: 9260 \nCity: Gistrup \nMaximal number of participants: 15 \nDeadline: August 25\, 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/design-and-query-processing-in-specialized-data-management-systems/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250623
DTEND;VALUE=DATE:20250626
DTSTAMP:20260414T212142
CREATED:20250310T103819Z
LAST-MODIFIED:20250310T103819Z
UID:10001485-1750636800-1750895999@ddsa.dk
SUMMARY:Convex Optimization Game Theory and Machine Learning for Upcoming 6G Networks
DESCRIPTION:Welcome to Convex Optimization\, Game Theory\, and Machine Learning for Upcoming 6G Networks \nDescription: Nowadays\, wireless networks have faced an explosive growth of data traffic because of the dramatic increase in the use of mobile devices and\, consequently\, data-greedy and delay-sensitive applications. Furthermore\, bringing everyone and everything unconnected to the connected world is crucial. Thus\, researchers in both industry and academia have introduced various promising technologies\, such as aerial networks\, integrated space-air-ground (ISAG) networks\, and reconfigurable intelligent surfaces (RIS)(both active and passive RISs)-assisted wireless networks\, simultaneously transmission and reflection (STAR) RIS-assisted wireless networks\, integrated sensing and communication (ISAC)\, and semantic communication\, to fulfill the traffic demands and provide the seamless wireless connectively in the upcoming generation of wireless networks (i.e.\, 6G networks). However\, we must overcome several research challenges\, e.g.\, how to integrate non-terrestrial networks with the existing terrestrial networks not only to provide seamless wireless connectivity but also to improve the spectrum and energy efficiency in the ISAG networks\, how to design optimal phase-shift in the RIS- and STAR-RIS-assisted wireless networks\, how to integrate communication and sensing function in the same infrastructure\, how to optimize beamforming design\, and how to optimize the spectrum allocation between these two functions in ISAC\, and among others\, before deploying those technologies in the real world. Fortunately\, methodologies such as convex optimization\, game theory\, and machine learning algorithms will help us to overcome challenges. Thus\, in this course\, we first comprehensively review the technologies appearing in 6G networks. Secondly\, we give the theory background of convex optimization\, game theory\, and machine learning algorithms. Finally\, we discuss how to implement those algorithms for cross-layer design optimization in the technologies appearing in 6G networks. \nPrerequisites: The students must have the basic knowledge of linear algebra\, probability and statistics\, ordinary differential equations (ODE)\, partial differential equations (PDE)\, and wireless networking. \nLearning objectives: The main objective is to introduce the technologies appearing in 6G networks and use convex optimization\, game theory\, and machine learning algorithms for cross-layer design optimization in the technologies appearing in 6G networks. \nOrganizer: Yan Kyaw Tun \nLecturers: Yan Kyaw Tun \nECTS: 3.0 \nTime: 23-25 June 2025 \nPlace: Aalborg University (Room: TBA) \nZip code: 9220 \nCity: Aalborg \nMaximal number of participants: 30 \nDeadline: 2 June 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/convex-optimization-game-theory-and-machine-learning-for-upcoming-6g-networks/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250619
DTEND;VALUE=DATE:20250621
DTSTAMP:20260414T212142
CREATED:20250310T103438Z
LAST-MODIFIED:20250310T103438Z
UID:10001560-1750291200-1750463999@ddsa.dk
SUMMARY:Urban Data Management Representation and Mining 2025
DESCRIPTION:Description: Urban computing is an emerging field that aims to address the challenges of rapid urbanization\, such as pollution\, energy consumption\, and traffic congestion. Urban computing involves acquiring\, integrating\, and analyzing large and heterogeneous data generated by a variety of sources in urban spaces\, including sensors\, devices\, vehicles\, buildings\, and people\, to tackle major urban challenges. \nThe course will introduce \n1) urban data management\, including urban data\, spatial data indexing\, spatial data query processing\, and learned spatial indexes; \n2) urban data mining\, including spatial data mining\, spatiotemporal prediction\, and reinforcement learning; \n3) geospatial entity representation for point objects\, trajectories\, and regions and their applications\, including speed inference\, region population estimation\, etc.; \n4) foundation models for geospatial applications. \nPrerequisites: Bachelor’s and master’s degrees in computer science or software engineering\, including knowledge on machine learning and data management as introduced in typical undergraduate courses. \nLearning objectives: The objective of the course is to provide students with a working understanding of basic knowledge\, as well as research problems and solutions of urban computing. \nOrganizer: Christian S. Jensen \nLecturers: Professor Gao Cong\, Nanyang Technological University\, Singapore \nECTS: 2 ECTS \nTime: June 19 – 20\, 2025 \nPlace: Aalborg University \nZip code: 9220 \nCity: Aalborg \nMaximal number of participants: 15 \nDeadline: May 29\, 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/urban-data-management-representation-and-mining-2025/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250609
DTEND;VALUE=DATE:20250611
DTSTAMP:20260414T212142
CREATED:20250305T145644Z
LAST-MODIFIED:20250305T145644Z
UID:10001562-1749427200-1749599999@ddsa.dk
SUMMARY:Distributed Data Processing with Dataflow Systems (2025)
DESCRIPTION:Description: In today’s world\, data is at the heart of decision-making processes across various domains. Dataflow is a programming paradigm and execution model that underpins many modern distributed data processing systems. In this model\, developers create programs by defining sequences of functional transformations on input data. The system runtime then manages the execution of these programs across distributed computing infrastructures\, abstracting away complexities related to development\, distribution\, communication\, and fault tolerance. \nThis course delves into the fundamental concepts of dataflow systems\, covering both programming models and implementation details. Starting with basic constructs for analyzing static and streaming data\, the course progresses to more advanced topics such as iterations\, time-based computations\, and user-defined functions. We will explore and compare different approaches to implementing these constructs\, highlighting their respective advantages and disadvantages. \nThroughout the course\, students will engage with examples from modern  dataflow systems and participate in hands-on sessions to complement the theoretical notions. \nPrerequisites: Familiarity with Java \nLearning objectives:  \nOn successful completion of this course\, students will be expected to be able to: \n1. Gain a comprehensive understanding of the dataflow paradigm\, its significance in distributed data processing systems and the use cases where it can be used. \n2. Design and implement dataflow programs that efficiently process large volumes of data in real-time. Master both basic constructs for static and streaming data analysis and advanced topics such as iterations\, time-based computations\, and user-defined functions. \n3. Evaluate dataflow systems\, understand the various performance metrics\, design and execute sound experiments. \n4. Compare the existing dataflow frameworks\, understanding the relative advantages and disadvantages. \nOrganizer: Daniele Dell’Aglio \nLecturers: Alessandro Margara\, Politecnico di Milano \nECTS: 2.0 \nTime: 9 – 10 June 2025 \nPlace: Aalborg University \nZip code: 9220 \nCity: Aalborg \nMaximal number of participants: 25 \nDeadline: 19 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/distributed-data-processing-with-dataflow-systems-2025/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250513
DTEND;VALUE=DATE:20250604
DTSTAMP:20260414T212142
CREATED:20250305T142426Z
LAST-MODIFIED:20250305T142426Z
UID:10001518-1747094400-1748995199@ddsa.dk
SUMMARY:Radio Simulation Techniques
DESCRIPTION:Welcome to Radio Simulation Techniques \nDescription:  \nDue to the increasing complexity of radio systems\, the use of advanced simulations is becoming one of the preferred methodologies for realistic performance assessments. In line with this statement\, the leading industrial standardization forum for cellular systems – the Third-Generation Partnership Program (3GPP) – also relies heavily on advanced simulations when developing and benchmarking innovative features for new releases. However\, for radio simulation approaches\, theoretical models\, and related methods\, for instance to improve the runtime of simulations\, continue to play an important role\, and therefore analytical methods are often an integrated part of simulations. \nThis PhD course will focus on simulation techniques relevant for advanced radio system simulations to obtain realistic performance results. This will be exemplified through use cases with dynamic system-level simulations (SLSs) of 5G and beyond cellular radio systems. A key ingredient for simulation is selection of proper models to make sure that the performance determining effects are properly reflected for realistic radio performance results. \nMany models and related methodologies for various use cases will be presented. Our focus is on generally accepted models that are largely supported by academia and industrial players\, and adopted by 3GPP\, as being realistic. This includes deployment models\, radio propagation models\, traffic models\, non-terrestrial cellular networks with satellites\, methodologies for Machine Learning (ML) enabled air-interface solutions\, and many more. \nWe also present several recommendations for best practices related to preparing\, running and interpreting simulation campaigns. This includes considerations of how to ensure that produced simulation results are statistically reliable and have the desired accuracy to draw trustworthy conclusions. \nThe course will also touch upon agile software engineering considerations for radio simulations. This is relevant since adopting good software practices are becoming increasingly important in connection with systems like 5G\, 5G-Advanced\, and 6G. These more extensive systems triggers more complex and elaborate SLS tools and use of different platforms and software libraries. \nThe course will aim at providing an intuitive understanding of the described models and methodologies\, including pointers to relevant open source 3GPP documents and selected IEEE publications. Example results are used whenever feasible\, with recommendations for running actual simulations. Active participation is required as a basis for the course evaluation. \nPrerequisites: \nProbabiliy and statistics\, stochastic processes\, Matlab/Python programming \nLearning objectives:  \nSimulation methodology\, simulation scenarios from standardization\, simulation models\, statistical analysis of simulation results\, parameter search\, best practises \nOrganizer:  \nKlaus I. Pedersen \nLecturers:  \nKlaus I. Pedersen\, Troels Pedersen\, Troels B. Sørensen \nECTS: 1.5 \nTime: 13/5\, 15/5\, 20/5\, 22/5\, 27/5\, 3/6 2025 \nPlace: Aalborg University \nZip code: 9220 \nCity: Aalborg \nMaximal number of participants: 15 \nDeadline: 23 April 2025 \nImportant information concerning PhD courses:  \nThere is 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 the 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 of the course. \nWe cannot ensure any seats before the deadline for enrolment\, all participants will be informed after the deadline\, approximately 3 weeks before the start of the course. \nTo attend courses at the Doctoral School in Medicine\, Biomedical Science and Technology you must be enrolled as a PhD student. \nFor inquiries regarding registration\, cancellation or waiting list\, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you. \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/radio-simulation-techniques/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250331
DTEND;VALUE=DATE:20250405
DTSTAMP:20260414T212142
CREATED:20250305T135142Z
LAST-MODIFIED:20250305T135142Z
UID:10001559-1743379200-1743811199@ddsa.dk
SUMMARY:Reinforcement Learning 2025
DESCRIPTION:Description: An intelligent system is expected to generate policies autonomously to achieve a goal\, which is mostly to maximize a given reward function or minimize a given cost function. \nReinforcement learning is a set of methods in machine learning that can produce such policies. To learn optimal actions in an environment that is not fully comprehensible to itself\, an intelligent system can use reinforcement algorithms to leverage its experience to figure out optimal policies. Nowadays\, reinforcement learning techniques are successfully applied in various engineering fields\, including robotics (DeepMind’s walking robot) and computers playing games (AlphaGo and TD-Gammon). \nDeveloped independently from reinforcement learning\, dynamic programming is a set of algorithms in optimal control theory that generate policies assuming that the environment is fully comprehensible to the intelligent system. Therefore\, dynamic programming provides an essential base to learn reinforcement learning. The course aims at building a fundamental understanding of both methods based on their relations to each other and on their applications to similar problems. \nThe course consists of the following topics: \nMarkov decision processes\, dynamic programming for infinite time and stopping time\, reinforcement learning\, and verification tools for reinforcement learning. \nPrerequisites: Basic knowledge of mathematics: calculus and probability \nLearning objectives:  \n– General Introduction to machine learning herein Reinforcement Learning \n– Markov Decision Processes and Dynamic Programming \n– Reinforcement Learning with Temporal-Difference Learning \n– Policy prediction with approximation \n– Verification tool UPPAAL for model-based reinforcement learning \nKey literature: TBA \nOrganizer: Rafal Wisniewski \nLecturers: Kim Guldstrand Larsen\, Zheng-Hua Tan\, Rafal Wisniewski\, Marius Mikucionis\, Abhijit Mazumdar \nECTS: 2.0 \nTime: 31 March to 4th April 2025 \nPlace: Aalborg University (Room: TBA) \nZip code: 9220 \nCity: Aalborg \nMaximal number of participants: 40 \nDeadline: 10 March 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/reinforcement-learning-2025/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241104
DTEND;VALUE=DATE:20241107
DTSTAMP:20260414T212142
CREATED:20240424T103028Z
LAST-MODIFIED:20240424T103028Z
UID:10001115-1730678400-1730937599@ddsa.dk
SUMMARY:Human-Centered Artificial Intelligence (2024)
DESCRIPTION:Welcome to Human-Centered Artificial Intelligence \nOrganizer: Niels van Berkel \nLecturers: Haiyi Zhu \nECTS: 3 \nDate/Time: 4-5-6 November 2024 \nDeadline: 13 October 2024 \nMax no. Of participants: 20 \nDescription: Artificial Intelligence has experienced a tremendous increase in attention in recent years across all sectors in society ranging from health\, transportation\, finance\, construction\, entertainment among others. Taking an optimistic view\, Ben Schneiderman envisions\,” computing devices that dramatically amplify human abilities\, empowering people and ensuring human control.” He proposes that\, “Human-Centered AI (HCAI)\, enables people to see\, think\, create\, and act in extraordinary ways\, by combining potent user experiences with embedded AI support services that users want.” Taking departure in this view\, we explore prevailing research and discuss the important issues related to how AI system predict and monitor and adapt to the user. Research themes include the staples of HCI such as task performance and usability/experience and HAI-specific concerns about transparency\, explainability\, predictability\, user control\, and ethical implications. \nHCI research is a fundamentally interdisciplinary field that is growing and rising to the challenges and opportunities with AI. In this course\, participants will learn a concise history of the topic of Artificial Intelligence\, understand the basic technical terms and techniques\, and will gain an overview of broad topics of current human-centered AI research. Examples from autonomous transportation\, voice interfaces\, robotics\, public information systems and others provide a deeper understanding of the state-of-the-art research and design of HAI. Students will learn how to critically examine HAI research articles identifying the strengths and weaknesses and possible future directions. Students will consider their current research and how themes of this course relate to their work. \nPrerequisites:  Students should be familiar with the basic methods and practice from Human-Computer Interaction\, Computer-Supported Cooperative Work\, or similar fields. \nLocation: All lectures will take place at Selma Lagerløfs Vej 300\, 9220 Aalborg – the Computer Science building (also called Cassiopeia) at Aalborg University. Easiest to take bus 2 or bus 12 to AAU Busterminal (Sigrid Undsetsvej / Aalborg) and walk a few minutes from here. We will start Monday in Room 0.2.90 \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/human-centered-artificial-intelligence-2024/
LOCATION:Selma Lagerløfs Vej 300                the Computer Science building\, Room 0.2.90\, Aalborg
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241007
DTEND;VALUE=DATE:20241010
DTSTAMP:20260414T212142
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:20240904
DTEND;VALUE=DATE:20240907
DTSTAMP:20260414T212142
CREATED:20240424T091254Z
LAST-MODIFIED:20240424T091254Z
UID:10001118-1725408000-1725667199@ddsa.dk
SUMMARY:Data and Machine Learning Operations (DataOps and MLOps) (2024)
DESCRIPTION:Welcome to Data and Machine Learning Operations (DataOps and MLOps) \nDescription:  The course will cover central concepts\, methods\, techniques\, and tools within DataOps and MLOps. Topics include Data Augmentation\, Labeling\, Cleaning\, Pre-processing\, Quantifying the Data Quality\, Lifecycle\, machine learning model deployment\, ML pipeline orchestration\, monitoring and maintenance (via updating with transfer learning OR retraining) in production\, ensemble algorithms\, and technical infrastructure. \nPrerequisites:  Bachelor and master degrees in computer science or software engineering\, including knowledge on machine learning and data management as introduced in typical undergraduate courses\, as well as significant practical experience with these topics. \n  \nOrganizer: Christian Thomsen \nLecturers: Alexandros Nanopoulos\, University of Hildesheim \nECTS: 2 \nDate/Time: 4 – 6 September 2024 \nPlace: Selma Lagerlöfs Vej 300\, room 0.2.90 \nZipcode: 9220 \nCity: Aalborg \nDeadline: 15 August 2024 \nMax no. Of participants: 15 \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/data-and-machine-learning-operations-dataops-and-mlops-2024/
LOCATION:Aalborg University                Selma Lagerlöfs Vej 300\, room 0.2.90\, Aalborg
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240602
DTEND;VALUE=DATE:20240629
DTSTAMP:20260414T212142
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:20260414T212142
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:20260414T212142
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