Sort by
Refine Your Search
-
capable of harbouring habitable conditions as we know them, understanding how their high-energy environments evolve is one of the most pressing open questions both in modern astrophysics and planetary
-
for Planetary HabitabilityCountryNorwayGeofield Contact City Oslo Website https://www.mn.uio.no/phab/english/about/ Street PO box 1072 Blindern Postal Code NO-0316 E-Mail t.h.torsvik@geo.uio.no STATUS: EXPIRED X
-
branch through in-depth studies of selected parliamentary debates that express underlying values and assumptions about the rule of law, while judicial understandings of the rule of law will be examined
-
highly specialised software development group comprises about 20 full-time employees. More information is available at http://www.icgi.no and http://www.domore.no . The position is based in the Digital
-
objective of generating novel knowledge about how to reduce social inequality. The interdisciplinary center integrates researchers with substantive expertise from education, psychology, sociology, economics
-
the application. Details about the groups may be found using the following links: https://www.mn.uio.no/math/english/research/groups/several-complex-variables/index.html https://www.mn.uio.no/math/english/research
-
research community in exploring near-analog biologic brain inspired solutions to reduce power consumption in neural networks. In this project you will be involved in a collaborative effort investigating
-
Ulla Schildt/NHM 19th April 2026 Languages English English English Natural History Museum PhD Research Fellow in Systematic Botany Apply for this job See advertisement About the position A 4-year
-
, including AI-based methods. The project associated with this position aims to take advantage of a virtual library of more than 1 million organometallic bifunctional catalysts, optimised for hydrogenation and
-
related to models and multiple sources of data describing ecological dynamics. The PhD project will address the following aims: 1) Develop efficient tools for learning about models from data, 2