I am also recruiting postdocs through CSE Fellows Program and Schmidt AI in Science Postdocs Program.
CSE Fellows program has a rolling deadline. Schmidt AI program requires both a Science mentor and a AI mentor.
We invite applicants for a postdoctoral position on the LIVER project (Large-scale, Intelligent, Vascularized, Engineered, Regenerative liver). The role focuses on AI-driven methods for image processing, experimental design, and automated manufacturing of large-scale, multi-cellular bioprinted liver constructs. You will develop and apply machine learning techniques to: 1) extract quantitative features from microscopy and non-invasive imaging, 2) drive AI-assisted design for patient-specific constructs, and 3) close the loop between computational design and scalable bioprinting manufacturing.
The project combines automated experimental design, scalable 3D bioprinting, biophysical modeling, GMP-compliant iPSC generation, multi-lineage differentiation, and in vivo validation. The postdoc will lead development of AI modules for image-based phenotyping, automated defect detection, generative models for optimizing scaffold/vascular layouts, and biophysics-guided deep learning models that inform manufacturing design for reproducible, market-viable biofabrication.
We seek exceptional postdoctoral candidates hosted at UCSD Computer Science & Engineering , jointly advised by by Prof. Rose Yu , Taylor Berg-Kirkpatrick , Yian Ma and Duncan Watson-Parris to start as soon as possible.
Current methods in AI for scientific discovery are ad-hoc. Separate models need to be developed for different scientific experiments and fine-tuned for different prediction tasks. Furthermore, these methods are still limited to uni-modal data, low-dimensional experiments, and lack physical or scientific reasoning.
The goal of the research is to build new foundation models for scientific discovery that can be the basis of an autonomous scientist. This effort envisions an autonomous scientist possessing the ability to characterize its own uncertainty and skepticism, and use them as drivers to systematically acquire and refine its scientific knowledge bases, in a way that human scientist partners can trust.
We seek exceptional postdoctoral candidates hosted at UCSD Computer Science & Engineering by Rose Yu , in collaboration with General Atomics to start as soon as possible.
Simulating complex partial differential equations (PDEs) is a fundamental task in science and engineering. However, it often suffers from high-computational cost and cannot generalize to PDEs with different boundary and initial conditions. Deep learning has emerged as a powerful tool to streamline and accelerate scientific simulation, e.g. [Wang et al. 2020]. But challenges still exist in ensuring the physical consistency of the solution and in handling complex geometries.
The goal of the research is to develop novel hybrid deep learning algorithms and models for solving complex PDEs, especially for gyrokinetic simulations of plasma turbulence in magnetic fusion. The project will explore interdisciplinary techniques in scientific computing, deep learning, and turbulence modeling to help speed up the modeling and simulation progress in fusion and plasma science, with the ultimate goal of realizing of a fusion pilot plant on a decadal timescale.
We seek exceptional postdoctoral candidates to be hosted at UCSD/HDSI, Columbia, or UCI. These positions have an initial term of one year with the possibility of extension. The starting date is flexible, but no later than Fall 2022.
The past two decades have witnessed natural disasters and extreme weather events that affect millions of lives. At the same time, the data volume from high-resolution climate models, satellite, in-situ and ground-based measurements have substantially increased to petabyte scales. These new and readily accessible datasets create the previously missing pipeline for scientific machine learning (ML), which in turn can improve our understanding and ability to predict extreme climate events.
This project will develop deep latent variable models (LVMs) to discover hidden physical structures in high-dimensional, spatiotemporal data of extreme climate events such as droughts or heatwaves, see Project Website for details.