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Field
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microbial community behaviors. We seek to understand and engineer the spatiotemporal behaviors of biological networks using tools from systems and synthetic biology. A major goal is to design novel strategies
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lab focuses on the characterization of plant signaling networks that steer plant specialized metabolism within tightly regulated fitness programs, in particular those modulated by stress hormones
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manipulating these bacteria, allowing scientists to study and control their unique physiology and metabolism at an accelerated pace, with far reaching impacts on sustainable biomanufacturing and human health
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developing adaptive numerical schemes powered by advanced nonlinear approximations—like Gaussian mixtures and neural networks. The key challenge? Designing robust and stable numerical schemes that remain
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(XAI) methods to improve the understanding of key drivers controlling peatland conditions and ecosystem functioning. The research project will primarily focus on implementing and merging analyses
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(XAI) methods to improve the understanding of key drivers controlling peatland conditions and ecosystem functioning. The research project will primarily focus on implementing and merging analyses
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lab focuses on the characterization of plant signaling networks that steer plant specialized metabolism within tightly regulated fitness programs, in particular those modulated by stress hormones
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with physics-informed neural networks, automatic differentiation, neural ODEs, or other physics-aware DL techniques. Skill in programming languages such as Python, C/C++, Go, Rust etc. Ability to model
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postdoctoral scholar. The lab focuses on delineating the epigenetic pathways that regulate normal hematopoiesis and to further determine how regulatory networks are perturbed in leukemia. Our current research
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neural networks. The key challenge? Designing robust and stable numerical schemes that remain efficient even in high dimensions, effectively pushing back against the curse of dimensionality. The ideal