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funded. The successful applicant will work in Professor Carlos Argüelles’ group on the development, construction, and deployment of TAMBO Phase 1. Candidates with strong expertise in data acquisition
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that capture the temporal evolution of complex chronic diseases from sequential medical images (plus clinical information) to predict plausible outcomes for patients on and off treatments. Driving innovative
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, LROC, ROC, Image perception, mathematical/computational observer models. Both experimental and computational positions available. Skills preferred include any of these: Monte Carlo simulations, benchtop
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financial dynamics Apply machine learning and Monte Carlo techniques to simulate complex decision scenarios Contribute to a growing, interdisciplinary field that redefines biodiversity through the lens
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group (https://bckrlab.org). We focus on high impact applications and work on knowledge-centric AI and biomedical machine learning including multi-omics integration, single cell analysis, and sequential
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enzymatic reactions. It will also involve combining enzymatic reactions with chemical catalysis or separation, sequentially or in one pot setups. Required skills will include high throughput enzyme
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: The candidate will be involved in the following tasks: 1) analysis of sequential data; 2) statistical analyses, 3) presentation of research results (conference/seminars), and 4) preparation of publications in
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Ph.D. in Physics or a closely related field is required. Candidates with a strong background in quantum many-body theory and experiences in quantum Monte Carlo and tensor network methods are encouraged
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significant additional guidance by Assoc. Prof. Carlo Bortolotti at UNIMORE (https://personale.unimore.it/Rubrica/dettaglio/cabortol ). If you are passionate about advancing the field of organic bioelectronics
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radar-derived surface soil moisture product using sequential data assimilation for root zone soil moisture mapping at high spatial resolution. Key Responsibilities: Assessment of different land surface