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Field
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. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic analysis, facilitating the discovery of efficient and sustainable synthetic routes for complex
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uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments. • Collaborate with mathematical modelers and experimentalists in
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performance testing (e.g., polarization curves, EIS). Design and execute experiments for materials optimization and degradation analysis. Maintain and operate laboratory equipment, including potentiostats
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, Probabilistic Inference, Algebraic Topology and Wavelet analysis theory. Familiar with Matlab/Python/C++ programming. Experience with Pytorch and multi-GPU model deployment. Experience in analyzing complex
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Department of Chemical and Biological Engineering at Princeton University. The position is in the broad area of renewable energy systems synthesis, analysis, and optimization. The goal of the project is to
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control for iPSC lines. • Establish and optimize experimental protocols and analytic pipelines for assays (e.g., imaging-based biomarker assays, metabolic flux assays), multiomics profiling, and
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. The research interests include superconductivity, topological physics, fundamental condensed matter physics at extreme conditions, high static pressure technology, and development of novel diagnostic
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optimization of community PV systems, e-mobility, and edge data center integration at the Swiss pilot site hosted at Empa’s NEST demonstrator. The role involves coordinating the deployment and validation
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: - Quantum computing with qudits, quantum error correction and fault-tolerance - Quantum optics of trapped ions and Rydberg atom arrays - Numerical tensor network techniques - Topological order and (de
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multiplex and multilayer networks alongside with the observed links in order to predict or reconstruct the missing links. The first step is to explore different optimization methods using low rank tensor