Welcome to Unmanned Aerial Vehicles (UAVs or Drones) in Research and Industry
Description: This comprehensive three-day course immerses PhD students in the rapidly evolving domain of drone technology. This course highlights the current applications of drones in various industries and research areas, offering a blend of lectures, critical analysis of contemporary studies, hands-on experiences with UAV technology, and engaging discussions that aim to integrate drone technology into the students' existing research projects.
In this modern era, the drone industry has blossomed, finding pivotal roles in sectors such as agriculture, surveillance, logistics, and environmental monitoring, among others. This course is designed to foster a deep understanding of the latest advancements and potentials of UAV technology, empowering participants to innovate and integrate drones into their research initiatives. Central to the course is the belief that drones, when harnessed effectively, can significantly augment human capabilities, offering innovative solutions and opportunities in various professional fields.
During this course, participants will delve deep into the multifaceted world of UAVs, acquiring insights into the fundamental technical terminologies and methodologies associated with drone technology. The curriculum is further enriched by the insights and expertise of a panel of distinguished guest lecturers, categorized into two groups: academic researchers and industry experts.
Learning objectives: The objective of the course is to provide PhD students with a comprehensive understanding of the current research and industry applications of drone technology. Students will gain hands-on experience with UAV operations, explore critical case studies, and engage in design-thinking exercises that address technical, regulatory, and ethical challenges. The goal is to equip students with the skills and insights necessary to integrate drone technology into innovative research projects and industrial applications.
Prerequisites: There are no technical or subject matter-related prerequisites for participants.
Organizer: Timothy Merritt
Lecturers: External lecturers include experienced professionals from industry and academic research. External lecturers include experienced professionals from industry and academic research institutions. Lecturers are coming from Denmark, France, Austria, and Thailand.”
ECTS: 2
Time: 26, 27, and 28 May 2025
Place: Aalborg University, Copenhagen Campus, A. C. Meyers Vænge 15, 2450 København
Zip code: 2450
City: Copenhagen
Maximal number of participants: 20
Deadline: 5 May 2025
Important information concerning PhD courses:
There 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.
We 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.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at phdcourses@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Chandhawat BOONYARD
- Teacher: Anke Brock
- Teacher: Anders Christensen
- Teacher: Timothy Robert Merritt
- Teacher: Toke Suhr
Welcome to Human Centered Artificial Intelligence
Description: Artificial 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."
This 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.
The course is structured to provide an interactive and comprehensive learning experience, utilizing Liberating Structures to facilitate active engagement and collaboration. Participants will:
- Understand the foundational principles of human-centered AI and their application in software engineering.
- Explore strategies for designing AI systems that prioritize human needs, transparency, and equity.
- Develop practical skills in implementing and evaluating human-centered AI technologies.
- Reflect on the ethical implications and societal impact of AI in software engineering.
By 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.
Liberating Structures: Liberating Structures (LS) are a collection of easy-to-learn interaction methods that enhance relational coordination and trust among participants. Unlike traditional frontal lecturing, LS methods focus on interactive workshops where everyone participates actively. These structures enable all participants to contribute to shaping the direction and outcomes of the course by structuring how people interact and collaborate. LS methods are designed to include and unleash everyone’s contributions in ways that are productive and engaging. Key Liberating Structures used in this course include:
- Impromptu Networking: A rapid process for initiating connections and conversations between participants, fostering quick and meaningful engagement.
- 1-2-4-All: A structured way of having everyone generate ideas and share them, starting individually, then in pairs, groups of four, and finally with the entire group.
- TRIZ: A problem-solving method that involves identifying everything that could lead to the worst possible result, and then flipping these to find innovative solutions.
- Conversation Café: A lightly structured method for hosting group dialogues that encourage thoughtful and inclusive conversation.
- Open Space Technology: A participant-driven process where individuals propose and lead sessions on topics they are passionate about.
Prerequisites: None.
Organizer: Daniel Russo
Lecturers: Daniel Russo
ECTS: 3 ECTS
Time: 27, 28, 29 October 2025
Place: Aalborg University Copenhagen
Zip code: 9220
City: Copenhagen
Maximal number of participants: 20
Deadline: 6 October 2025
Important information concerning PhD courses:
There 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.
We 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.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at phdcourses@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Daniel Russo
Welcome to Design and Query Processing in Specialized Data Management Systems
Description: 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.
Prerequisites: 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).
Learning 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.
Organizer: Wolfgang Lehner
Lecturers: Wolfgang Lehner
ECTS: 2 ECTS
Time: September 16 - 18, 2025, from 9-17 (Tuesday, Wednesday, 9-13 (Thursday)
Place: Room 02.90, Selma Lagerløfsvej 300, Aalborg University
Zip code: 9260
City: Gistrup
Maximal number of participants: 15
Deadline: August 25, 2025
Important information concerning PhD courses:
There 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.
We 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.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at phdcourses@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Wolfgang Lehner
Welcome to Urban Data Management, Representation and Mining
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.
The course will introduce
1) urban data management, including urban data, spatial data indexing, spatial data query processing, and learned spatial indexes;
2) urban data mining, including spatial data mining, spatiotemporal prediction, and reinforcement learning;
3) geospatial entity representation for point objects, trajectories, and regions and their applications, including speed inference, region population estimation, etc.;
4) foundation models for geospatial applications.
Prerequisites: 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.
Learning 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.
Organizer: Christian S. Jensen
Lecturers: Professor Gao Cong, Nanyang Technological University, Singapore
ECTS: 2 ECTS
Time: 30 June, 1 July, 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 15
Deadline: 9 June 2025
Important information concerning PhD courses:
There 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.
We 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.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at phdcourses@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Christian S. Jensen
Welcome to Distributed Data Processing with Dataflow Systems (2025)
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.
This 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.
Throughout the course, students will engage with examples from modern dataflow systems and participate in hands-on sessions to complement the theoretical notions.
Prerequisites: Familiarity with Java
Learning objectives:
On successful completion of this course, students will be expected to be able to:
1. Gain a comprehensive understanding of the dataflow paradigm, its significance in distributed data processing systems and the use cases where it can be used.
2. 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.
3. Evaluate dataflow systems, understand the various performance metrics, design and execute sound experiments.
4. Compare the existing dataflow frameworks, understanding the relative advantages and disadvantages.
Organizer: Daniele Dell'Aglio
Lecturers: Alessandro Margara, Politecnico di Milano
ECTS: 2.0
Time: 9 - 10 June 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 25
Deadline: 19 May 2025
Important information concerning PhD courses:
There 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.
We 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.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at phdcourses@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Daniele Dell'Aglio
Welcome to Quantum Computing
Description: The course will provide an introduction to quantum computing (e.g. qubits, measurements, quantum circuits etc.) and classic quantum algorithms such as Grover's quantum search algorithm. Afterwards, the course will demonstrate how classic computer science approaches can be used in the field of quantum computing.
Prerequisites: Basic linear algebra
Learning objectives: Quantum basics, quantum circuits, quantum search, simulation and verification of quantum circuits.
Organizer: Max Tschaikowski
Lecturers:- Kim G. Larsen
- Torben Larsen
- Christian Schilling
- Max Tschaikowski
ECTS: 2 ECTS
Time: 12,13,14,15,16 May 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 30
Deadline: 21 April 2025
Important information concerning PhD courses:
There 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.
We 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.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at phdcourses@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Kim Guldstrand Larsen
- Teacher: Torben Larsen
- Teacher: Marius Mikučionis
- Teacher: Marco Antonio Muniz Rodriguez
- Teacher: Christian Schilling
- Teacher: Max Tschaikowski
- Teacher: Rafal Wisniewski
Welcome to Trusted AI methods for sequential decision making (2025)
Description: The theme of this course is automatic reasoning and learning, with a focus on decision making and in particular sequential decision making. These techniques are relevant whenever repeated interaction with an environment is required. As an example, consider the task of moving a robot from A to B. While a complete path can be statically computed, if the environment is dynamic and uncertain, the planned path can become inaccessible. Thus, on-the-fly adaptation is required.
This course will introduce a number of algorithmic solutions such as planning and safe reinforcement learning, explaining the algorithmic principles behind several offline and online sequential decision-making approaches.
The course will consist of several lectures as well as some hands-on exercises.
Prerequisites: You will need background in a mathematical discipline; in particular, you should have attended a course covering propositional logic. You also need basic programming experience (read and write simple computer programs) and familiarity with standard algorithms like search algorithms. Basic familiarity with machine learning is useful but not required.
Learning objectives: The students are familiar with algorithmic solutions to sequential decision making.
Key literature: TBA
Organizer: Christian Schilling
Lecturers: Alvaro Torralba and Christian Schilling
ECTS: 2.0
Time: 10, 12, 17, 19 and 26 March 2025
Place: Aalborg University (Room: TBA)
Zip code: 9220
City: Aalborg
Maximal number of participants: 20
Deadline: 17 February 2025
Important information concerning PhD courses:
There 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.
We 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.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at phdcourses@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Christian Schilling
- Teacher: Alvaro Torralba