Department of Computer Science, Aarhus University’s Post

𝑾𝒆 𝒂𝒓𝒆 𝒍𝒐𝒐𝒌𝒊𝒏𝒈 𝒇𝒐𝒓 𝒏𝒆𝒘 𝑷𝒉𝑫 𝒔𝒕𝒖𝒅𝒆𝒏𝒕𝒔 𝒕𝒐 𝒋𝒐𝒊𝒏 𝒕𝒉𝒆 𝒅𝒆𝒑𝒂𝒓𝒕𝒎𝒆𝒏𝒕 👩🏻🎓👨🏻🎓If you're passionate about pushing the boundaries of computer science, we invite you to apply! The application deadline is November 1. Here’s an overview of the available positions: 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐚𝐧𝐝 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐟𝐨𝐫 𝐅𝐚𝐢𝐭𝐡𝐟𝐮𝐥 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐀𝐈 (𝐅𝐗𝐀𝐈) Focus on developing new explainable AI models and algorithms that offer iterative and interactive explanations, bridging machine learning and human-computer interaction. Supervisors: Prof. Ira Assent and Prof. Marianne Graves Petersen 𝐄𝐧𝐬𝐞𝐦𝐛𝐥𝐞 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐏𝐫𝐨𝐠𝐫𝐞𝐬𝐬𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 Explore ensemble visualizations to interactively manage multiple streams of data analysis for progressive analytics. The project will be carried out in collaboration with colleagues from the University of Pittsburgh (concurrent software architectures) and AU Engineering, Aarhus University (artifact detection in timeseries data). Supervisors: Assoc. Prof. Hans-Jörg Schulz and Assoc. Prof. kaare mikkelsen 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧 𝐃𝐞𝐬𝐢𝐠𝐧 𝐟𝐨𝐫 𝐅𝐚𝐢𝐭𝐡𝐟𝐮𝐥 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐀𝐈 (𝐅𝐗𝐀𝐈) Research how to design interactive explanations for AI that are usable and meaningful, engaging closely with real-world cases in journalism and education. Supervisors: Prof. Ira Assent and Prof. Marianne Graves Petersen ✨ Can't find a perfect fit? Submit a proposal via our general call if you have a great PhD project idea ➡️ https://lnkd.in/eVsHGuJM. For more information about research areas at the department, see ➡️ https://cs.au.dk/research Don't miss the chance to be part of groundbreaking research in AI, HCI, data visualization, and more! #ComputerScience #openpositions #AI #DataVisualization #HCI 

𝗣𝗵𝗗 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗰𝗮𝗱𝗲𝗺𝘆 𝘀𝗲𝘀𝘀𝗶𝗼𝗻 𝗼𝗻 𝗖𝗮𝘀𝗲 𝗦𝘁𝘂𝗱𝘆 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝘄𝗶𝘁𝗵 Hila Lifshitz We hope you had a great start to the new year! 🎆 Before the Christmas holidays, the inaugural year of the PhD Research Academy ended with a highly informative session. Hila Lifshitz shared her expertise on „Pitfalls and advantages of case study research: When is it worth going to the wild?“. Thank you, Hila, for so freely sharing your extensive experiences! We talked about different types of field research such as (n)ethographies, interviews, and field experiments, and discussed advantages and pitfalls of time-constraint field research settings such as hackathons. Our main takeaways: 🌟 𝗪𝗵𝗲𝗻 𝘁𝗼 𝗴𝗼 𝘁𝗼 𝘁𝗵𝗲 𝗳𝗶𝗲𝗹𝗱? Field research is especially suitable for studying 𝙣𝙤𝙫𝙚𝙡 𝙥𝙝𝙚𝙣𝙤𝙢𝙚𝙣𝙖 for which we don’t yet have archival data, studying phenomena 𝙞𝙣 𝙙𝙚𝙥𝙩𝙝, and for understanding processes in real-life settings. 👩🔧 𝗛𝗼𝘄 𝘁𝗼 𝗴𝗼 𝘁𝗼 𝘁𝗵𝗲 𝗳𝗶𝗲𝗹𝗱? First, identify the right 𝙧𝙚𝙨𝙚𝙖𝙧𝙘𝙝 𝙦𝙪𝙚𝙨𝙩𝙞𝙤𝙣 where field research can make a difference (see above). Second, experiences show that field researchers 𝙜𝙚𝙩 𝙞𝙣 𝙩𝙤𝙪𝙘𝙝 𝙬𝙞𝙩𝙝 6-10 𝙥𝙤𝙩𝙚𝙣𝙩𝙞𝙖𝙡 𝙛𝙞𝙚𝙡𝙙 𝙨𝙞𝙩𝙚𝙨 before gaining access to a suitable field. Third, field research is about 𝙧𝙚𝙡𝙖𝙩𝙞𝙤𝙣𝙨𝙝𝙞𝙥 𝙗𝙪𝙞𝙡𝙙𝙞𝙣𝙜, i.e., earning trust and immersing yourself in the field to truly understand the behavior and roles of the entities you study. 🧐 𝗛𝗼𝘄 𝘁𝗼 𝘄𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝗳𝗶𝗲𝗹𝗱 𝗱𝗮𝘁𝗮? Directly 𝙣𝙤𝙩𝙞𝙣𝙜 𝙫𝙚𝙧𝙗𝙖𝙩𝙞𝙢 𝙨𝙩𝙖𝙩𝙚𝙢𝙚𝙣𝙩𝙨 while or after talking to people in the field is vital to ensure transparent data collection. Annotations allow for understanding this data after years have passed, e.g., in a review process. 𝙏𝙝𝙚𝙢𝙖𝙩𝙞𝙘 𝙖𝙣𝙙 𝙨𝙪𝙗𝙨𝙚𝙦𝙪𝙚𝙣𝙩 𝙘𝙤𝙣𝙛𝙞𝙧𝙢𝙖𝙩𝙞𝙫𝙚 𝙖𝙣𝙖𝙡𝙮𝙨𝙚𝙨 are useful for analyzing the field data, e.g., based on a 𝙜𝙧𝙤𝙪𝙣𝙙𝙚𝙙 𝙩𝙝𝙚𝙤𝙧𝙮 approach.   Although field research takes its time and the data may seem messy at some points, field researchers have a lot of freedom and can cover the spectrum from inductive to deductive research. More information on the PhD Research Academy is available on our website: https://lnkd.in/g3GGdDnu

Dear Connections: Yesterday, I posted an ad on LinkedIn regarding postdoctoral positions in my group. Within just 24 hours, I received nearly 100 applications. Unfortunately, many applicants did not read the ad carefully. It was clearly stated that candidates should submit their details in a single PDF via email with the title “Postdoctoral Application 2025.” While several applicants followed the guidelines, many others overlooked them. A good scientist must be a good listener and a careful reader. Therefore, I urge all aspirants to read the ad thoroughly before applying for any position elsewhere. Consider why you are the best fit for the job and how this opportunity will advance your career. Always bear in mind that time is money, and utilizing your time effectively can help you achieve your aspirations. Now, I wanted to share this screenshot for your reference... AI is a useful tool, but please use it thoughtfully. The purpose of my message is to educate the young stars who represent the future of our scientific community. My intention is not to discourage anyone, but rather to encourage personal growth and development by showing these minor mistakes anyone makes in a rush that negatively impacts the outcome. We all possess an inherent strength at birth, but it is the way we nurture and refine that strength that ultimately shapes who we become. Each individual's journey of self-improvement is crucial, and it is through dedication and effort that we can fully realize our potential.

Q: What systematic approach do scientists use to explore everything from the tiniest atoms to the vastness of space? 🤔 A: The scientific method: Make an observation - First, you make an observation of some phenomenon that cannot be explained fully using current knowledge. Do background research and ask a question - Then, based on this observation and background research, you ask a question about the phenomenon that you would like to answer. Generate a hypothesis - At this point, you propose a hypothesis, or a testable explanation to your question. Conduct an experiment - Then, you design an experiment in order to test your hypothesis. Analyze data and draw conclusions - From this data, draw conclusions about the validity of your hypothesis. If the results do not support your hypothesis, you would propose a new one and test that. With each step, scientists gather knowledge, test hypotheses, and draw conclusions, driving forward our understanding of the world around us. Explore “The Scientific Method” pathway to learn how to think like a scientist! https://ow.ly/Fikc50RgsWm #LabXchange #ScienceEducation #SciEd

Interested in pursuing a PhD dealing with supporting deep uncertainty, anti-fragility, future-proof decisions and multiobjective optimization? Then apply to this fresh doctoral researcher position and join our group to develop better decision-support tools! #antifragility #deepuncertainty #uncertainty #decsisionsupport #decisionmaking #multiobjective #optimization #phd #openposition

Together with Netherlands eScience Center and the support of many international colleagues, we are going to create a unified, well-validated and open-source pRF software package that will foster innovations in psychology and neuroscience!

How to enable AI-driven advancements agriculture? With inter-disciplinary partnerships! Wageningen University & Research #AI group and the Netherlands eScience Center join forces to combine deep learning with process-based models to address food security applications from breeding to precision agriculture and climate change! Thanks to Michiel Kallenberg, Allard de Wit and the #AgML community of AgMIP – Agricultural Model Intercomparison and Improvement Project Really excited to start this new project together!

Interesting times ahead as we are going to build IceGraph, an emulator for ice sheet models using graph networks (IceGraph), together with the Netherlands eScience Center in a new Open eScience project. 

Living Systems Symposium - Call for Registration and Abstracts – Deadline 31st October! There’s still time to register and submit your abstracts for the Living Systems Symposium, taking place on 13th November at The Alan Turing Institute in London and online! We invite PhD students and Early Career Researchers to present their innovative research on mathematical, AI, and data-driven approaches in living systems modelling. Topics of interest include, but are not limited to: Interpretable machine learning models Multi-scale data integration Complexity algorithms Probabilistic programming Time series analyses Computer vision Agent-based modelling Deep learning This symposium offers a fantastic opportunity to share your work, connect with other early career researchers and experts, and contribute to important discussions in this area of research. Submission Deadline: 31st October Event Date: 13th November, 10:00–16:00 (GMT) We look forward to your contributions! https://lnkd.in/eciQcPHm #LivingSystems #MachineLearning #ComplexSystems #ConputationalModelling #MathematicalModelling #DataScjence

The newest edition of the NPA's POSTDOCket is so on point to my personal postdoctoral experience...Highly recommend it! https://lnkd.in/gChuek-X #postdoclife