Tidligere arrangementer - Side 4
Quantum Computing and Many-Particle Problems
Tor Ole Odden:
"The field of educational research has a massive literature base, with many journals that have been publishing articles for almost a century (or longer). How do we sort through and make sense of literature at this scale? We have begun using an unsupervised machine learning technique from the field of natural language processing, known as latent Dirichlet allocation, to analyze articles from the fields of physics education research and science education research. This technique allows us to extract latent themes, or topics, from the literature and quantify the rise and fall of those topics over time.
In this talk, I will present the basics of the technique, describe some of its underlying theory and applications, and showcase some of the trends that it reveals in how science education theory and practice has evolved over the last 20-100 years."
Quantum Dynamics, Many-body methods and basis sets
Doctoral candidate John Mark Aiken at the Department of Physics, Faculty of Mathematics and Natural Sciences, is defending the thesis "Understanding University Student Pathways Towards Graduation with Machine Learning and Institutional Data" for the degree of Philosophiae Doctor.
Quantum Computing: Many-Body Methods and Machine Learning
Generating artificial electrophysiological recordings with neuron action potentials using a GAN-network
Modelling and analysis of EEG signals
Using Machine Learning to Recreate Signals from the Primary Visual Cortex of Mice
Using Deep Reinforcement Learning for Active Flow Control
Computer science was originally invented as a tool to support learning in other disciplines, including engineering and economics. Today, most of computer science education is aimed at preparing future software developers. How do we broaden the appeal of and access to computer science education, to something closer to what the inventors of the field had in mind?
Practical work like laboratory work and fieldwork is integrated parts of many science educational programmes. However what do students learn through practical work?, and is it the same in different types of practical work?
Welcome to a company presentation by two former master students of the CS program now working at S & T - with many interesting job possibilities for everybody who loves Computational Science.
The ScienceAtHome group at Aarhus University has developed a number of games and tools for quantum-physics-based citizen science and education. One of these tools, Quantum Composer, allows students and researchers to explore quantum mechanics in one dimension.
We have developed a framework to describe the modeling process in physics laboratory activities.
This talk investigates what it means to learn computer science content, how we might better support computer science learning, and how we might better understand what learners know.
"Classical Molecular Dynamics using Neural Network Representations of Potential Energy Surfaces"
"Studies of Quantum Dots using Machine Learning"
"Latent Variable Machine Learning Algorithms: Applications in a Nuclear Physics Experiment"
The presentation touches on learning goals, assessment, and teaching practices around computation and discusses research that has been carried out in the context of P-Cubed that has informed our thinking and resulted in iterations on our design.
Velkommen til det årlige juleseminaret vårt! I år fokuserer vi på beregninger og programmering i begynnerundervisningen i fysikk og integrasjon av programmering i matematikk i fagfornyelsen i skolen fra 2020. Programmet vil også inkludere en offisiell åpning av de nye lokalene til senteret.
A curriculum for the introductory calculus-based course taken by beginning university science and engineering students, takes a contemporary perspective on introductory-level physics.
Pizza and gaming night/evening afterwards. You are all very welcome.
Today’s speaker is Gaute Einevoll, a computational neuroscientist. He will present possible master thesis topics as well.
Although quantitative approaches to data generation, collection and analysis are common in physics education research (PER), they are frequently misunderstood even among veteran scholars in the field.