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, policy, energy conversion, new business models, techno-economic and life cycle analyses, machine learning, optimization, AI, intelligent networks, among others. The PDF will join a project in collaboration
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: Machine learning/deep learning model development for biomolecular data analyses and prediction Research Area: Data science and computational chemistry Required Skills: A Ph.D. in relevant field within
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on advanced machine learning applications in computational pathology, medical imaging, and clinical text analysis. They will be expected to develop deep learning models for analyzing whole-slide histopathology
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deep learning techniques for analyzing high-dimensional omics data, identifying predictive biomarkers, and understanding cancer heterogeneity. Projects include modeling of bulk and single-cell
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Energy Modelling and Simulation. The Dworkin group focuses primarily on numerical modelling, but also performs some experiments as needed for certain projects. The group currently has 2 Postdoctoral
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functional genomics, CRISPR-based screening, or single-cell technologies. Hands-on experience with next-generation sequencing library prep and tissue culture models. Demonstrated ability to conduct independent
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lead the in vivo functional analysis of this gene using conditional KO mouse models, with a focus on brain development, neuronal differentiation, and seizure susceptibility. All models are already
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understood, modeled, and ultimately reversed or rebuilt using bioengineering and synthetic biology approaches. The successful candidate will: Investigate mechanisms of T-cell development, aging, and thymic
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-cell compartment, develops and deteriorates with age, and how these processes can be understood, modeled, and ultimately reversed or rebuilt using bioengineering and synthetic biology approaches
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to develop data-driven, space-time explicit precision agronomic solutions Utilizing high-performance computing (HPC) systems for large-scale geospatial data processing, model training, and validation Designing