Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
: PhD junior researcher) to collaborate with the funded line of research “Structural Neural Networks”. Reference: I-PI 43-26 Pursuant to the provisions on the regulations governing calls for applications
-
neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view learning, transfer learning, and data fusion techniques to integrate heterogeneous omics datasets
-
with ab initio calculations or mineral equations of state; familiarity with MAGEMin, BurnMan; knowledge of Bayesian methods (MCMC) or neural networks applied to geophysical problems; publications in
-
on developing hybrid traffic flow models that combine physical modelling principles with machine learning approaches, such as Physics-Informed Neural Networks (PINNs) and machine-learning-enhanced traffic models
-
subjects. For further details about the University, please visit PolyU’s website at https://www.polyu.edu.hk/ . DEPARTMENT OF BIOMEDICAL ENGINEERING Assistant Professor in Sports Science (Ref. 260724014
-
subjects. For further details about the University, please visit PolyU’s website at https://www.polyu.edu.hk/ . DEPARTMENT OF BIOMEDICAL ENGINEERING Assistant Professor in Brain Machine Interface (Ref
-
Brandenburgische Technische Universität Cottbus | Cottbus, Brandenburg | Germany | about 2 months ago
to the development and the analysis of modern machine learning methods, with a focus on probabilistic modelling that enables, for example, to account for uncertainties when training neural networks, to capture complex
-
relevant to the project, namely in the development and application of neural networks to mechanical engineering problems, and excellent understanding and writing of the Portuguese language and the
-
learning or neural networks), etc. The experience could come from lithium-ion, lithium-ion capacitor, sodium-ion, or other battery technologies. You have solid skills in Matlab/Simulink simulation tool
-
Biology: Development of deep learning, graph neural network, generative AI, and RNA foundation models to analyze large-scale omics, spatial transcriptomics, ribonomics, and imaging datasets. Application