Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
specific requirements: a) Experience in the application of data analysis and machine learning methods to scientific data (e.g. multivariate analysis, chemometrics, neural networks, classification
-
, and EHR data. Experience with modern deep learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, Pandas, NumPy, and SciPy. Familiarity with convolutional neural networks (CNNs), graph
-
such as transformers, self-supervised learning, multimodal learning, generative models, graph neural networks, or foundation models. Experience with structural and/or functional brain modeling. Familiarity
-
will join a community of exceptional scientists working on diverse topics ranging from how organisms age or how our DNA is repaired, to how epigenetics regulates cellular identity or neural memory
-
opportunities at CIBR's official website: https://en.cibr.ac.cn/home At CIBR, we pride ourselves on our interdisciplinary approach, collaborating closely with top hospitals to address critical questions in brain
-
of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the MR signal into the training of the INR network, we aim to compensate for the effects
-
network, and six centers and institutes that focus on cancer treatment and research, neuroscience, clinical and translational research, advanced biotechnology and medicine, environmental and occupational
-
. Key Responsibilities Research & Development: Integrate Physics-Informed Neural Networks or Reinforcement Learning to create realistic human movement and interactive social behaviors within XR
-
cell states and cell fate. Current directions include spinal cord development and regeneration, stem cell responses to spinal cord injury, neural stem cell transplantation, CNS organoids, and
-
determines observables of the replication program such as the Mean Replication Timing (MRT) and the Replication Fork Directionality (RFD) profiles. We proposed a strategy to train a neural network to infer