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& Partnerships (NSF-TIP) directorate. More information on the project is available at: https://industriesofideas.ai/ . Term-limited: This is a term-limited position for two years, with the possibility of renewal
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physics, Bayesian inference, and complex systems theory. You will contribute to method development, simulation and validation in close collaboration with experimental partners. Key Responsibilities: Carry
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maximum of ONE student per project. This process will ensure an excellent fit of student to project and also an excellent strategic fit of the project within the faculty. Project titles: Bayesian methods
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. This PhD will focus on uncertainty-aware machine learning models, developing and evaluating techniques (e.g., Bayesian and interval neural networks) to quantify model uncertainty and monitor it during
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, Uncertainty quantification, Approximation Theory, Applied Probability and Bayesian statistics, Optimal Control and Dynamic Programming. Appointment, salary, and benefits. The appointment period is two years
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of the future, striving to improve healthcare and society as a whole. Where to apply Website https://www.academictransfer.com/en/jobs/356668/phd-in-machine-learning-for-dru… Requirements Specific Requirements
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spectrographs with various spectral resolutions, operating from 0.5 to 28 µm. Our group has developed the Bayesian modeling tool FORMOSA (Petrus et al. 2023). It allows the inference of low-resolution (R = λ/Δλ
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implementing models that integrate ecological dynamics, species traits, phylogenetic trees, and economic discounting; ● Devising Bayesian or POMDP frameworks to handle uncertainty about species interactions
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learning models such as Bayesian optimization, neural networks, random forests. A high proficiency in spoken and written English. Excellent communication and interpersonal skills. You are expected to learn
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position within a Research Infrastructure? No Offer Description https://www.isterre.fr/ You will join ISTerre's GRE team, which consists of around twenty people in Grenoble, and will mainly contribute