Formålet med projektet er at øge konkurrenceevnen og kvaliteten af Kamstrups ultralydsflowmålere ved at reducere tryktab, forøge signal-til-støj forholdet i ultralydssignalet, reducere flow-forstyrrelser i indløbet. Samtidig reduceres kostprisen og udviklingstiden for nye flowmålere markant. Dette opnås gennem numerisk fluiddynamik, optimeringsmetoder og kvantificering af usikkerheder, med henblik på at kunne udvikle optimerede flowmålere hurtigere.

Metodeudvikling til privat landinspektørfirma til mere effektiv proces og ejendomsdannelse i 3D – ejendomme under/over hinanden - ud fra en juridisk, teknisk og samfundsmæssig kontekst. Øget efterspørgsel på byrum under/over jorden samt bæredygtighed øger krav til ejendomsgrænser i 3D og retlige ejendomskonstruktioner. Ejendomsregistreringen – matrikel/tingbog mv. -udfordres af moderne digital forvaltning. Projektet inddrager praksis i Danmark, internationale erfaringer og nyeste teknologier.

In this research programme we will develop and validate state-of-the-art Artificial Intelligence based models for Breast Cancer (BC) routine histopathology classification and for improved patient stratification in respect to prognosis and treatment response. Through both retrospective and prospective validation of the developed models, we will establish evidence towards future clinical translation. Our studies are based on large-scale population samples (>35,000 whole slide images), ensuring unbiased data and models. Novel methodologies for stain-free and multi-stain analysis will also be developed, which has the potential to contribute to improved models and advancing the field of digital pathology. The project aims to improve the quality of BC histopathology assessments by reducing errors and inter-assessor variability, enhancing patient stratification and reducing over- and under-treatment of patients, while also contributing toward more efficient/reliable routine pathology.