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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
abiotic resources. We integrate remotely sensed information with in-situ data, process-based models, and leverage satellite communication, IoT and machine learning technologies in order to provide evidence
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experience Desirable criteria Up to date knowledge of machine learning methods applied to clinical or omics data Up to date knowledge in long read methylation methods applied to clinical or omics data
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CeMM - Research Center for Molecular Medicine of the Austrian Academy of Sciences | Austria | 2 months ago
-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Open Postdoc
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
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energy consumption in information processing and machine learning (e.g., arXiv:2308.15905); Quantum phenomena in information processing: exploring how quantum effects can be utilized to process information
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to conduct applied research (TRL>1) in the domain of quantum computing and/or machine learning; Possibility to file patent applications within the project; Funds to employ 3 other researchers: 1 postdoc and 2
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and uncertainty mapping at satellite, airborne and drone levels. You will explore advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and
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biology skills Experience with single-cell RNA-seq analysis Experience with machine learning based methods Have evidence of scientific accomplishment via peer-reviewed publications Understanding of cancer
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middleware (e.g., ROS, MoveIt) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic arms (e.g
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, and registry-linked outcome data. In this project, you will develop and apply AI-based methods (e.g. machine learning methods and many other methods) to harmonize historical and current pathogen