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Description Completion of doctoral thesis related to: Process and analyze experimental data. Develop predictive models using deep learning. Train, validate, and optimize neural networks (CNNs, etc.) applied
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Lab applies rigorous evaluation and modeling methods, including natural and field experiments, randomized controlled trials, behavioral economics, and machine learning, to help policymakers identify and
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constraints such as electromagnetic interference (EMI), thermal stability, and mechanical durability. In parallel, the project will refine and optimize existing machine learning models for fault detection and
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degree in computer science (or a related field) Rich experience in devising machine learning models, methods, and algorithms for computer vision and image processing. Scientific track record with
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computational analyses of single-cell, spatial transcriptomics, and multi-omics datasets Developing and maintaining reproducible, well-documented analysis pipelines Applying and adapting machine learning and AI
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, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving
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complex, high dimensional and high-volume datasets. Uses data preparation, modeling and predictive modeling, analysis, processing, algorithms, and systems. Applies knowledge of statistics, machine learning
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to analyze data and experience with statistical, machine learning, and data science approaches. Prior experience working in teams on collaborative projects. Knowledge, Skills and Abilities: Expertise in one
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programming such as Python, R, MATLAB, or other similar programs and experience in using simulation/optimisation models and advanced data handling techniques e.g. machine-learning techniques, statistics
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, particularly radionuclides, on a continental scale. The aim is to develop a new class of inverse Bayesian models, STE-EU-SCALE, combining innovative forward dispersion models, machine learning techniques, and