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California State University, Northridge | Northridge, California | United States | about 9 hours ago
plus. Experience with advanced analytics, including predictive modeling, data science, or statistical analysis to support data-driven decision-making. Demonstrated experience designing and implementing
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Application deadline: All year round Research theme: Environmental geochemistry How to apply: https://uom.link/pgr-apply-2425 This 3.5-year PhD studentship is open to EU, UK, and US applicants. The
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process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support process and
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algorithms for dynamic master selection, coordinating BESS, PV, diesel generators, and other sources. Implement predictive, rule-based, or optimisation-based control strategies using MATLAB/Simulink, Python
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learning, generalization/robustness and privacy aspects in scalable learning algorithms. Large‑scale optimization and control: Optimal control, model predictive control and other optimization‑based control
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knowledge of process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support
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of existing studies to promote the use of risk-informed decision frameworks, prediction models, AI applied to planetary protection. Tasks include: Support the creation of probabilistic models for planetary
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processing, quality control, integration, and analysis of single‑cell and multimodal omics datasets (e.g. scRNA‑seq, scATAC‑seq). Train, evaluate, and benchmark deep learning models operating on single‑cell
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processing, quality control, integration, and analysis of single‑cell and multimodal omics datasets (e.g. scRNA‑seq, scATAC‑seq). Train, evaluate, and benchmark deep learning models operating on single‑cell
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regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty bounds and deriving