Sort by
Refine Your Search
-
real-world applications in green chemistry and industrial synthesis. Key Responsibilities: Develop and implement AI/ML models (e.g., graph neural networks, transformer-based models) for retrosynthetic
-
represented by a graph, which is a collection of nodes that are connected to each other by edges. The nodes represent the objects of the network and the edges represent relationships between objects. A common
-
of Machine Learning (Theory or Practice). A successful candidate will be expected to lead a research team of graduate students as well as teach at the undergraduate and graduate levels. The position is open to
-
Sciences (Theory or Practice). A successful candidate will be expected to lead a research team of graduate students as well as teach at the undergraduate and graduate levels. The position is open to
-
learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view
-
: Holomorphic functions, residue theorem, and their application to optical system analysis. Differential Geometry: Study surfaces and curves in optical materials using tensor calculus. Group Theory: Using
-
on propulsion systems for small satellites, with emphasis on chemical, electric, and green micro-propulsion systems. They will also teach propulsion theory and lab-based courses, and contribute to CubeSat mission
-
Cluster Contact: Pr. Johan Jacquemin – johan.jacquemin@um6p.ma Research Activities Develop independent research programs bridging experimental and Density Functional Theory (DFT) simulation of materials
-
theory and its transformative applications. Why Join the UM6P Vanguard Center? The UM6P Vanguard Center offers a unique environment that bridges the gap between theoretical research and impactful
-
CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
metabolomics data from clinical studies. Apply deep learning models (e.g., autoencoders, variational autoencoders, graph neural networks) for biomarker discovery, disease classification, and patient