Forsknings­arrangementer

Kommende arrangementer

Tid og sted: , Det Norske Videnskaps-Akademi, Drammensveien 78, Oslo

What does it takes to create and sustain environments that support the development of high-level research projects?

Tid og sted: , Kristen Nygaards sal (5370)/ Zoom, Ole-Johan Dahls hus

Doctoral candidate Ines Petra Junge at the Department of Informatics, Faculty of Mathematics and Natural Sciences, is defending the thesis Good design doesn’t cost the Earth: How sustainability in ICT is enabled, encouraged, engaged, and exemplified by means of the mobile phone for the degree of Philosophiae Doctor.

Tid og sted: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor

his talk discusses a nonparametric inference framework for occupation time curves derived from wearable device data. Such curves provide the total time a subject maintains activity above a given level as a function of that level. Taking advantage of the monotonicity and smoothness properties of these curves, we develop a likelihood ratio approach to construct confidence bands for mean occupation time curves.  An extension to fitting concurrent functional regression models is also developed. Application to wearable device data from an ongoing study of an experimental gene therapy for mitochondrial DNA depletion syndrome will be discussed. Based on joint work with Hsin-Wen Chang (Academia Sinica).

 

Tid og sted: , Niels Henrik Abels hus, 9th floor

The frictional behavior of surfaces is a problem of great scientific and practical significance. Recent progress in molecular scale modeling allows us to determine the coefficient of friction for nanoscale surfaces from first principles using molecular dynamics modeling. However, inverse design, that is, designing surfaces with specific frictional propeties is still a complex and largely unsolved challenge in part due to the enormous space of possible surface configurations. Here, we demonstrate how we can use physical forward modeling to find the frictional properties of a set of surfaces that can serve as a training set to design machine learning models. In this talk, we demonstrate both discriminative and generative models for frictional surface design and analyze what physical principles the machine learning models have learned in this process.

 
Tid og sted: , Nucleus, Bikuben, Kristine Bonnevies hus

PhD candidate Renate Marie Alling at the Department of Biosciences, Faculty of Mathematics and Natural Sciences, is defending the thesis 'Environmental and genetic factors affecting endosperm-based post-zygotic hybridization barriers' for the degree of PhD.