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closely linked to a related opening at IMD-1 at Forschungszentrum Jülich within the same project consortium: https://www.fz-juelich.de/en/careers/jobs/2025-361 Please feel free to apply for both available
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algorithms, electric and magnetic fields, ultrasound, optics and targeted radiation, microfluidics, controlled force sensing and actuation and related tools for probing and controlling biomolecular systems
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Reference Number AE2025-0420 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2025-0420.pdf CALL FOR
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databases. Design, implementation, and testing of deep learning and AI algorithms for processing tabular, genomic and temporal data. Where to apply Website https://www.uam.es/uam/investigacion/ofertas-empleo
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preparing libraries for next-generation sequencing (NGS) as well as nanopore sequencing, along with the ability to analyze sequencing data using bioinformatics algorithms. Proficiency in developing algorithms
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. Create predictive algorithms for the occurrence of cyanobacteria blooms; 3. Support field activities, process large volumes of data, and contribute to scientific publications. Requirements: • PhD in areas
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an excellent publication record. Solid research experience in one or more of the following topics is expected: Graph neural networks Optimization algorithms Predicting structured output Self-supervised learning
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cohorts and data generation resources through highly collaborative clinical faculty. Ideal candidates will have expertise in computational modeling, machine learning, or algorithm development, with
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National Aeronautics and Space Administration (NASA) | New York City, New York | United States | about 16 hours ago
transfer algorithms for NASA GISS‘s general circulation model (GCM) to study radiative interaction and feedbacks between various atmospheric constituents and the climate system. Potential specific topics
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined