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Field
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intelligence. Our research spans computer vision, machine learning, and natural language processing, focusing on multimodal learning, data fusion, spatial-temporal modeling, and vision–language models. We study
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responsibility for carrying out research in rough path theory, machine learning, generative AI and related fields as part of the DataSig II grant “High order mathematical and computational infrastructure
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or kidney disease (SICKMEDS) project. The project aims to evaluate the impact of SICK-MEDS education on reducing acute kidney injury, hospitalisation rates in culturally and linguistically diverse people
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2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description As part of a
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Recognised Researcher (R2) Application Deadline 12 Oct 2026 - 21:59 (UTC) Country Finland Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not
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computational teams Access to unpublished validation data unavailable to existing agents State-of-the-art computing capable of running dozens of local large language models concurrently Data from emerging
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of the annual NOAA Hurricane Field Program to collect novel observations. Department Specific Functions Duties include but are not limited to: • Develop clearly written code and comments to analyze observational
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, speech, and audio research, including Aalto University’s large-scale scientific computing cluster with CPU and GPU nodes, access to CSC’s national computing infrastructure including LUMI, and the Aalto
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grant project “Driving innovation in crop resilience through Comparative QTLomics.” The selected candidate will contribute to five main objectives: 1. Apply large language models (LLMs) to collect
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generalisable and cognitively plausible. The project aims to understand how children build grammatical categories (such as nouns and verbs) from early language input, and the computational work will closely