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cells Key methods will include: Gaussian Processes (heteroscedastic & multivariate) Operator-valued and deep kernels Active Bayesian experimental design Physics-informed neural networks Closed-loop
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screens with robust guide demultiplexing and assignment, and cell-type annotation using bespoke references. Strong grounding in statistics (GLMs, hierarchical/Bayesian modeling, multiple testing) and
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) distributional generalization, transfer learning, causality Multi-objective settings and alignment, RL theory Statistical learning theory, optimization (e.g., implicit bias) Robustness (broadly defined), privacy
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/Seurat, count models, batch correction, differential analyses). Strong grounding in statistics (GLMs, hierarchical/Bayesian modeling, multiple testing) and experimental-design principles. Bioinformatics
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between Gramazio Kohler Research (Chair for Architecture and Digital Fabrication) at ETH Zurich and the Chair for Timber Structures (Prof. Dr. Andrea Frangi). Job description The objective of this PhD
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. Research proposal with clear statement of objectives, expected outcomes, and schedule, including how your project would link to ongoing research and facilities at ITA or LUS (max. 2500 words excl. references
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include: Motivation letter outlining your interest in the position and relevant qualifications. Research proposal with clear statement of objectives, expected outcomes, and schedule, including how your
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). Outline for the Proposed Research Project (4 pages + addenda). This document must include: Research title Abstract State-of-the-art review Clearly defined research questions Aims and objectives Anticipated
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work on designing novel Smart Sensors & Energy Efficient Machine Learning on Microcontrollers. The objectives of this thesis include: Design and prototype modular, low-cost sensor nodes integrating
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-armed Bandits, Bayesian Optimization. Automated Model Design and Tuning: Neural Architecture Search, Hyperparameter Optimization. Computer Networking: Resource-Constrained Networking (e.g., Internet