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accessible measure of location. One option is to use skew versions of know distributions that leave the mode or median of the distribution at the origin. The main objective of this project is to explore, from
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Areas (Codes 25–29) 1. Machine Learning (Code 25) Objectives: Support UFABC’s undergraduate and graduate programs, strengthen research in Machine Learning, and expand English-taught course offerings
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quality, avoiding the paralysis that troubles artificial algorithms when options seem equally good. This project asks: what objective functions do such biological systems optimise, and how can we use
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. Training will cover modelling, quantitative analysis, and laboratory methods. OBJECTIVES O1. Build a spatiotemporal lineage atlas of the pre-implantation human embryo. The student will assemble high-content
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 11 days ago
on models uncertainty. Currently, Breed method uses importance sampling technique and loss statistics. In the beginning, the objective is to get familiar with the domain and read about existing work
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, less dependent on a strict observation model, and better adapted to both interferences and very low SNRs. Objective – Topic 1: Explore how the statistics and geometry of noise in the time–frequency
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design, such as Bayesian Adaptive Clinical trial design or established expertise in statistical methods such as structural equation modeling, causal data analysis. Experience in serving in protocol review
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to facilitate the accomplishment of biodiversity conservation research objectives. Develops and writes new proposals to secure contracts for grant-funded research related to biodiversity conservation and the use
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
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key role in delivering the following objectives: Develop and validate advanced cardiovascular risk prediction models, including multi-outcome and dynamic models tailored to complex, multimorbid