Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
materials. Candidates should have a PhD in condensed matter experimental physics, mesoscopic electron transport, or experimental solid-state quantum information science. The project combines nanofabrication
-
on their assessment. You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/ . Interviews with selected candidates are expected to be held in week 43. Questions For specific
-
process at https://employment.ku.dk/faculty/recruitment-process/ The applicant will be assessed according to the Ministerial Order no. 242 of 13 March 2012 on the Appointment of Academic Staff
-
the opportunity to comment on the part of the assessment that relates to the applicant him/herself. You find information about the recruitment process at: https://employment.ku.dk/faculty/recruitment-process/ The
-
learning/AI for simulation and prediction of the outcome of process unit operations on food and ingredient functionality. Formulation and fluid dynamics of complex emulsions and the mechanisms governing
-
Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Department of Strategy and Innovation (SI) invites
-
industrial partners. In particular, you will: Develop and validate physics-based, data-driven, or hybrid digital twins of Electrolysis systems, capturing their electrochemical, thermal, flow, and system-level
-
process at https://employment.ku.dk/faculty/recruitment-process/. Interviews with selected candidates are expected to be held in late September 2026. Questions For specific information about the PhD
-
https://employment.ku.dk/faculty/recruitment-process/ . Interviews with selected candidates are expected to be held in week 42/43. Applicants must be prepared for further security screening related
-
while maintaining computational efficiency through lower-fidelity simulation of non-critical regions, (ii) virtual sensing techniques for load and stress estimation from limited and optimally placed