232 parallel-processing-"International-PhD-Programme-(IPP)-Mainz" Fellowship positions in Singapore
Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
for Sustainable proteins, who will be assessing in parallel the protein digestibility, bio accessibility and bioavailability of alternative proteins Key Responsibilities: To carry out analytical biochemical
-
part of a larger network, undergo persistent changes that ultimately lead to experience-dependent rewiring of the brain. In parallel to understanding how memories are formed, we are also keen to
-
: Proficiency in Python, PyTorch, JAX, or other ML frameworks - Computing: Experience with large-scale datasets, parallel computing, and GPUs/TPUs. - Algorithm Development: Ability to develop and optimize Machine
-
model is employed to forecast renewable energy availability, providing crucial insights for the design optimization process. The ML-assisted operation tackles the dynamic optimization of parallel energy
-
/ information/ proposal required for research-related grant call process Conduct experiments related to lipids and sporopollenin-based drug delivery Conduct interpretation and application of experimental results
-
Responsibilities: Electrochemical process on interface phenomena Battery testing under different conditions Simulation of scaled up process. Interface with machine learning group on data base set up Battery safety
-
, lamination, and testing. He/she will contribute to the development of new application driven materials and production processes, located mostly at Nanyang Technological University. Key Responsibilities: Lead
-
/ machine learning algorithms to support research in the IDMxS Analytics Cluster. The RF will apply/ improve machine learning algorithms to process (e.g., classify, predict) data collected by IDMxS. Help
-
, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
-
, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems