Sem Sælands vei 24
Sondre Vik Furuseth, Rosseland Centre for Solar Physics, UiO.
Anna Rogowitz (University of Vienna): Transforming a gabbro into an eclogite without fracturing - A tale of opening and closing fluid pathways
Jordi Bolibar (Utrecht University): Towards interpretable, physics-informed machine learning models for glacier evolution
The scientific program will include invited talks, selected oral contributions from submitted abstracts, and poster presentations, covering the following topics:
Martijn van den Ende (Université Côte d'Azur): Earthquakes, fibre-optic cables, and a zebra: intelligent solutions for tomorrow's seismology
Senior Engineer Sigrid Rønneberg, Justervesenet (The Norwegian Metrology Service)
Marcos Caballero: Integrating computation in American high schools - a tale of resourcing, federalism, and equity
Tor Ole Odden: Everything can be a vector! An approach to teaching machine learning to early physics students?
Julien Scheibert (École Centrale Lyon): Towards the design of contact interfaces with a specified friction law
Odd Petter Sand will defend his thesis Integrating Computing with Mathematics and Science Education: Case Studies of Student Understanding and Teaching design for the degree of Philosophiae Doctor.
Mathijs Janssen (University of Oslo): Electrolyte relaxation near electrified surfaces—from flat plates to nanoporous supercapacitors
Statistics courses typically come in two different flavors: 1) A first type of course seeks deep understanding based on mathematical reasoning and proofs. 2) A second type of course focuses on practical applications, primarily based on fixed recipes without delving into underlying details. 3) I will here propose a third way, which seeks understanding of fundamental concepts, but through programming and simulation instead of mathematical proofs. This third way is thus geared towards students that have a stronger background in programming than mathematics, and that seek to understand the fundamentals as a basis for developing statistical analyses and data science methodology. This third way would also exploit its basis in programming to promote self-driven exploration, drawing inspiration from how computing has been integrated into science education through the long-running CSE initiative. I will provide examples ranging from how to explore the central limit theorem to performing Bayesian marginalization.
By Professor Michele K. Dougherty
Department of Physics, the Blackett Laboratory, Imperial College London
Magnus Fagernes Ivarsen will defend his thesis Plasma Irregularity Dissipation in the F-Region Ionosphere. Making Sense of the Decay and Subsequent Lifetimes of Turbulent Plasma Structures in the Ionosphere for the degree of Philosophiae Doctor.
In this talk, I will briefly describe four different perspectives from the educational research literature on what it means to learn scientific computing: 1) modeling, 2) practices, 3) computational thinking, and 4) computational literacy.
I will also touch on the epistemologies inherent to each theory - what they have to say about scientific knowledge, cite key references, and suggest instructional approaches tailored to each approach.
Chris MacMinn (University of Oxford): Fluid-fluid phase separation in a soft porous medium
Bachelorstudenter på UiO fikk gå på en spesiell lab i vår. På skaperverkstedet LagLivLab dyrket de celler som de roterte med elektriske felt, stimulerte med elektroder til å bevege seg som robot-muskler og sugde inn i mikrofluidikk-brikker for å skape miniatyrinkubatorer.
Luke Zoet (University of Wisconsin-Madison): Investigating subglacial processes through seismicity and experimentation
Eric Larose (Université Grenoble Alpes): Environmental seismology : an emerging tool for probing slopes stability, rockfalls, and the evolution of the permafrost.
Renaud Toussaint (Université de Strasbourg/University of Oslo): Induced seismicity under Strasbourg: Possible mechanisms
Doctoral candidate Sisay Mebre Abie at the Department of Physics, Faculty of Mathematics and Natural Sciences, is defending the thesis
"Bioimpedance as a tool for monitoring the effect of freezing and thawing of meat"
for the degree of Philosophiae Doctor.
The 71st annual meeting of the Nordic Microscopy Society.
We are delighted to invite you to the EarthFlows June Meeting 2021.
Constructive alignment of learning aims, examination and learning activities is a convincing principle for course design. However, to make a meaningful alignment, we first need to precisely define and understand these aspects in the context of a given course. For instance, if learning aims are to truly guide/define examination and learning activities, we need a rich conception of learning aims that goes well beyond the short, general and typically vague formulations provided on course web pages. As always, devil is in the details. To initiate a discussion, I will show examples of how we have tried to face the challenge of being sufficiently concrete in our approach to constructive alignment in the course IN1000 (a large introductory programming course at UiO).
Friday 21.5, 1415-1500: Tor Ole Odden
Friday 28.5, 0915-1000: Kirsty Dunnett
After having taught computing in a mathematical setting for many years, I have collected a number of observations and hypotheses about how students develop their understanding. In particular I have seen many examples of how computing triggers understanding of mathematical concepts. Some of these are quite standard, eg. numerical integration as a tool to extend the understanding of the definition of the integral. In this talk I will discuss some fairly obvious examples of this kind, but I will focus on some less obvious examples. Hopefully, this will illustrate that computing can be integrated fruitfully with mathematics in areas far beyond what we do today.