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Modeling: Design, train, and validate generative machine learning models (GANs, VAEs, diffusion models, and LLM-based tabular synthesizers) to generate high-fidelity synthetic tabular, longitudinal, and
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-boundary MHD surrogates) for real-time predictions and control-oriented execution (<1 ms execution time). Advanced Control Synthesis: Synthesize and computationally test model-based (Model Predictive Control
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into high-purity powders. Using mechanochemical processing, you will investigate and control phase formation, particle size distribution, morphology, and flowability. You will then establish additive
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Strong command of behavior cloning, reinforcement learning, or related learning-based manipulation methods Proficiency in Python and modern deep learning frameworks (e.g., PyTorch) Proven ability
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. You will use human in vitro models, including astrocytes and brain organoids generated from induced pluripotent stem cells (iPSCs) of people with MLC and healthy controls. You will investigate disease
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This position involves working on a project related to the control of origami-based soft actuator modules for simple kinematic tasks. The objective of this position includes contributing
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cleaning and analyses, carrying data from raw files through analysis and interpretation Perform data quality diagnostics, including quality control of array-based data, handling of missing data, outlier
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to predictably control and exploit the drop for useful tasks. Aims: Develop computational models to quantitatively predict the response of chemically active drops to the various physico-chemical stimuli
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packages, data pipelines, and automation tools using Python, R, SQL, SAS, and other appropriate technologies. Apply modern software engineering practices, including Git-based version control, modular design
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project experience is acceptable. Experience with Hail, SQL or relational databases, Git/version control, cloud-based genomic analysis, PRS, Mendelian randomization, gene-environment interaction analysis