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using modern architectures such as convolutional neural networks, transformers, and diffusion models. Proven experience building AI solutions using classical ML algorithms such as decision trees, gradient
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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
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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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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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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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, Boston. Specific work hours will be determined by the supervisor. What you'll do Use existing clinical and imaging data (public or within hospital) to develop AI algorithms, apply them into research and
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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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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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National Aeronautics and Space Administration (NASA) | New York City, New York | United States | about 7 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