Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
geospatial workflows on an abstract level, using purpose-driven concepts and conceptual transformations; develop AI and machine learning based technology to automate the description and modeling of data
-
flexibility. To fully unlock this potential, we need advanced tools that digitally replicate these networks and support optimized design and data-driven control strategies. As our PhD candidate, you will
-
gold standard of geo-analytic purposes and questions; collaborate with a technical assistant, another PhD candidate (on geodata source modelling), and a postdoc (on the GeoQA reasoning engine). It is
-
: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
-
: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
-
sensing hardware into a high-throughput sorting line is a plus. Proficient in developing, training, and deploying AI-driven sensor-fusion pipelines, including preprocessing, deep-learning model
-
areas include, amongst others, personalised and adaptive systems, user modelling and recommender systems, human interaction with embodied AI, and persuasive technology. You will work closely with other
-
-driven methods to optimise large-scale renovation flows (Verbouwstromen). By bringing together contractors’ resources, clusters of buildings, and smart operations planning, PRE-MADONA accelerates
-
Organisation Job description In the Engineering and Technology Institute Groningen (ENTEG), we are looking for a talented and motivated PhD candidate on electrochemical ammonia synthesis in Protonic
-
candidate will have strong data-driven methodological learning opportunities with high social impact on cancer care organisation. They will work within an interdisciplinary team, applying advanced modeling