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Wrocław University of Science and Technology / Faculty of Information and Telecommunication Technology | Poland | 3 months ago
include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools for semantic search
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storage, but their widespread deployment is limited by challenges in energy density, stability, solubility, and cost of electroactive redox compounds. The PhD candidate will develop and apply machine
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. The scheduled start date is immediate. For work on site, the principal location will be the UOC Campus, at Rambla del Poblenou 154-156, Barcelona. At the UOC, we work with an open working model that combines
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: an insight from Genetics, Single-Cell Transcriptomics, and Machine Learning. Profile of the graduate Ph.D. graduate has extensive knowledge of cell and developmental biology, ranging from basic principles
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emphasizes principled modeling, reproducible experimentation with open datasets and simulations, and publication-ready contributions targeting leading venues in machine learning and wireless communications
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neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning rules in networks of complex spiking neuron models in the application field of geolocalization: Building
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will develop novel ligands for a validated drug target in acute myeloid leukemia. Building on our promising preliminary results, you will optimize and expand the hit discovery using computer-aided tools
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will develop novel machine learning and artificial intelligence (ML/AI) methods for genomics data, especially: large-scale single-cell genomics data, high-definition spatial genomics, digital pathology
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beneficial: Working knowledge of statistics and usage of MATLAB or other software for statistical analysis; Experience with machine learning and data mining. Good Estonian language skills Application procedure
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systems, and space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where