216 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "Université de Bordeaux " Postdoctoral positions in Denmark
Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration
-
systems. Further information on the Department is linked at https://www.science.ku.dk/english/about-the-faculty/organisation/ . Inquiries about the position can be made to Assoc. Prof. Leonardo Midolo
-
for applications, the authorized recruitment manager selects applicants for assessment on the advice of the Interview Committee. You can read about the recruitment process at https://employment.ku.dk/faculty
-
/or large genetic datasets. This may include genetic analyses, causal inference, epidemiological analyses, and clinical prediction modelling using machine learning approaches, and development
-
key agroecosystem variables. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and
-
Research Focus We are offering a Postdoctoral position in graph machine learning, algorithms, and graph management with particular focus on: Modeling real-world spatio-temporal energy networks Developing
-
, unlocking reliable perception and navigation where GNSS/GPS cannot be trusted or is unavailable. The project combines ultrasonic sensing, probabilistic perception, and machine learning with advanced robotics
-
key agroecosystem variables. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and
-
(e.g. using COBRApy or related toolboxes), or a strong motivation to develop this expertise. Data science, AI/ML, and digital surrogate models Experience with data science and machine learning, including
-
-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and