Sort by
Refine Your Search
-
. Additional qualifications Experience with one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models
-
increasingly rely on data-driven models to extract, represent, and interpret information from complex and evolving environments. Traditional machine learning approaches, as well as many classical signal
-
probabilistic forward model (a digital twin) that maps microstructure to electrochemical performance. This involves simulation-based inference and physics-informed machine learning techniques that can quantify
-
are looking for candidates with: An interest in developing mathematical methods and machine learning models good communication skills with sufficient proficiency in oral and written English, an excellent
-
will develop new methods for machine learning and dynamical systems, including generative modeling and system identification, with applications in biomedical modeling, large-scale autonomous systems
-
‑mining and machine‑learning methods. The expected scientific outcome is to establish guidelines for identifying and optimizing promising electrolyte materials and to support the development of future
-
) and Machine Learning/NLP (Natural Language Processing) to capture both the network embeddedness and the qualitative B2B relationship features of supply chains. The project identifies key bottlenecks