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Field
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on hierarchical Bayesian models that allow us to integrate heterogeneous, but complementary, ecological and environmental data. Depending on the background and interest of the candidate, the work will focus on a
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closely related field Proven research experience and publication track record in robotic manipulation, deformable object handling, or related domains Hands-on experience with robotic hardware (e.g., robot
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are seeking a postdoctoral researcher to develop methods for analyzing large scale biodiversity and ecosystem function data. Our approach is based on hierarchical Bayesian models that allow us to integrate
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health sciences and exposure assessment and fosters associated programs in environmental health education and public policy. Established in 1986, EOHSI’s major objectives are to: Advance understanding
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to our work, meaning that we set up experiments in the lab or online to measure behavioural performance (e.g. response times and accuracy) to tasks involving object recognition and categorisation. Our
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methods to ultimately let dermatologists continually update multi-modal machine learning models. Our research objectives are to 1) develop novel model editing methods for multi-modal models, with a focus on
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adults in South Africa, Zimbabwe, and Malawi. You are someone that is capable of keeping your team on track to meet deadlines and research objectives. You already have a publication record, and at IHME
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communication and collaboration skills Preferred: Experience with simulation-based inference and Bayesian methods Familiarity with cosmological simulations or observational cosmology ML architecture design and
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to peer reviewed journals and papers for presentation at conferences and workshops under the direction of the Principal Investigator or Grant-holder. To assist with the development of research objectives
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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP