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-0831 Description of Work: At the Digital Twin Innovation Hub, we are developing infrastructure for the construction, simulation, analysis, and visualization of a human immune system model that represents
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a PhD student, you will develop state-of-the-art learning and inference methods to detect and characterize anomalous radio behavior and to design algorithms that remain reliable under practical
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.pdf JOB ADVERTISEMENT Recruitment of 1 Doctorate in the field of Research and development of new algorithms for processing and classifying physiological signals in ambulatory systems INESC TEC
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-spectrometer synchronization, and spectral analysis algorithms, contributing to prototype validation in geological case studies. Objectives: - Develop and validate the galvanometer scanning system.; - Implement
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 2 months ago
systems. Key Responsibilities Develop graph-based (multi-)omics analysis algorithms Benchmark graph-theoretic against graph-ML approaches Analysis of food-related (multi-)omics data Your Profile The ideal
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analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical
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simulations on the Aurora supercomputer, using AMReX (https://amrex-codes.github.io/amrex/ ) and the lattice Boltzmann method (LBM). The candidate will develop flow/geometry-aware refinement strategies that go
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, the development and implementation of novel algorithms, machine learning for parsing biological data sets (genomics, proteomics, imaging, neuroscience), and related areas at the interface of computer
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
) algorithms, etc. and Google Map APIs - Familiarity with Jira or similar software for agile software development, team collaboration and project management - Proficiency in software version control and
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project focuses on developing ultra-reliable spatiotemporal (4D) predictions using trustworthy, distributed AI-driven intelligence deployed across heterogeneous aerial nodes. To achieve this, an aerial