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Field
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analysis in the Wasserstein space of stochastic processes (contact Johannes Wiesel [email protected] ) Causal modelling and related questions in reliable machine learning (contact Sebastian Weichwald
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scholar will develop statistical machine learning and artificial intelligence methods (ML/AI) for diverse biological data in relation to cardiovascular and neurodegenerative diseases. The specific areas
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The University of Helsinki ( https://www.helsinki.fi/en ), founded in 1640, is one of the world’s leading universities for multidisciplinary research. The university has an international academic
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machine learning, reduced-order modeling, or data-driven modeling of physical systems. You will conduct research on the development of fast, trustworthy, and data-efficient surrogate models that
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project will develop general self-supervised and unsupervised machine-learning methods for biological data where reliable labels are scarce, expensive, or impossible to obtain. The aim is to build methods
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databases, design and analysis of virus-derived RNA libraries, and development of machine learning models for detecting functional elements in viral metagenomic datasets. This project is a
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autonomous, closed-loop (“self-driving”) laboratory workflows. The role integrates catalyst synthesis, high-throughput reactor testing, and in situ/operando characterization with machine-learning and
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(Ithaca, NY) is seeking a creative, computationally driven Postdoctoral Associate to help invent the next generation of liquid biopsy technologies. The De Vlaminck Lab ( https
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The Ohio State University | about 2 months ago
analysis, machine learning, and data-driven modeling applied to large astronomical datasets and time-domain surveys is highly desirable. Although experience with microlensing and transit exoplanet
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, including the ultra-high-resolution Energy Exascale Earth System Model (E3SM), the machine learning emulators ACE-ERA5 and ACE-E3SM, and the regional Energy Research and Forecasting (ERF) model. The work