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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 10 days ago
the replicator equation. The candidate(s) may also be required to develop computational and algorithmic platforms to link models to biological data. The project integrates dynamical systems, graph theory, linear
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a diverse team of biological and chemical scientists Experience with high performance computing environment (HPC) / cluster job submission Knowledge of statistical methods, data science algorithms
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microscopy, 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
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for real-time human data processing in interactive settings. Technical expertise in areas such as electrophysiological recording, VR paradigm design, closed-loop algorithm development, or clinical
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in Summer 2026, for a term of 2 years with the possibility of an extension. The postdoc will join the ERC-Starting Grant project team on “Participatory Algorithmic Justice: A multi-sited ethnography to
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expertise in existing methods and state-of-the-art in the field. The position includes algorithm design, software implementation, and validation on experimental datasets. You will contribute to building a
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Postdoctoral Research Associate who will focus on creating innovative artificial intelligence algorithms for the trusted visualization of large-scale 3D scientific data. This position resides in the Data
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Description Overview: We are seeking a Postdoctoral Research Associate who will focus on creating innovative uncertainty quantification and visualization algorithms that enable trusted visual representation and
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application! Work assignments This position focuses on the development of theoretically grounded and practically scalable decentralized learning algorithms under realistic system constraints, including
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by integrating large-scale single-cell foundation models with structured biological knowledge encoded in genomic graphs. The project will also deliver efficient algorithms to train these models under