Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
algorithms and circuits to enhance imaging quality and speed Creating efficient data acquisition and processing workflows for large datasets of skin nanotexture images Optimizing hardware-software integration
-
skills in English, both written and spoken. Experience with project management or EU-funded projects is an advantage. Further information Further information may be obtained from Giovana Monteiro Gomes
-
technology and characterisation. Background within image processing, computer vision, or related fields. Background within in-line process monitoring or related fields. Furthermore, it is an additional
-
; mepage.faculty.ucdavis.edu ). Simonsen will be the main advisor to the PhD student. Her research studies policies that directly or indirectly affect children’s outcomes and she is an expert on the Danish administrative data
-
employment is 18 months. Starting date is 1 April 2026, or according to mutual agreement. Both positions are full-time positions. You can read more about career paths at DTU here . Further information Further
-
here . Further information Further information may be obtained from Associate Professor Stavros Gaitanaros (staga@dtu.dk ) or the head of the section, Associate Professor Christian Kim Christiansen
-
, thermodynamic models, and unit operations, developed as part of research projects at both CERE and KTC. These software tools include but are not limited to the Integrated Computer Aided System (ICAS), CAPCO2
-
-event data collection + feedback Assist with analyses and PowerPoint for ongoing projects. Support partner recruitment in our scaling phase (e.g., prospect research) SoMe knowledge is a plus but not a
-
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the
-
description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will