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genomic graphs. The project will also deliver efficient algorithms to train these models under budget and time constraints, facilitating flexible adoption of the methods. The project is carried out in close
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scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation Experience applying machine learning or AI to molecular simulation
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of computational biology, Genomics, machine learning, and data science, contributing to the development and evaluation of advanced algorithms for analyzing large-scale biological datasets. This role is ideal
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, and protein structure prediction by artificial intelligence algorithms. The goal is to generate functional models of multimeric protein complexes and how they assemble as a guide to understand disease
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University. This research opportunity will be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including bulk and single
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applications for a Post-Doctoral Associate position, in the area of Quantum Algorithms. The candidate is expected to conduct research in computer science focusing on the combinatorial aspects of quantum
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by combining psychological profiling, biological lab data, physiological time series, and sensor data. The postdoc will play a leading role in developing and implementing predictive algorithms designed
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to be developed: Analyze iEEG data. Develop multimodal algorithms. Perform the characterization of the epileptogenic network. Where to apply Website https://seuelectronica.upc.edu/en/procedures/call-for
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out research activities, including reports, studies and scientific articles; Creation of algorithms, code snippets and development of statistical methods to achieve the desired results; Collaboration
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to trace how welfare algorithms operate in practice. They will also contribute to comparative analysis across case study countries, support interdisciplinary collaboration, engage with external stakeholders