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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
but are not limited to: development of new AI architectures for biology and hybrid models that combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and
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epidemiology, and AI-accelerated drug and therapeutics discovery. DS/AI-driven Natural, Physical, and Earth Sciences, including physics-informed machine learning, climate and environmental modeling, physics and
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of foundational and agentic AI models (LLMs, generative AI, RAG-based architectures) for autonomous, context-aware decision-making. Researching and implementing trustworthy human-machine interaction methods
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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Engineering, we are seeking a researcher with a strong interest in developing and applying machine‑learning methods for materials design, in particular steel design. The position is part of our growing research
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/nanoplastics, and other environmental compounds to assess their potential impacts on human health and the environment using machine learning (ML), deep learning (DL), and big data analytics. His lab is
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. The postdoctoral researcher(s) will join an international research environment at Umeå University, including Stat4Reg (www.stat4reg.se ), which develops statistical and machine-learning methods for register data
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geoscientific process models, as demonstrated by presentations, publications and/or repositories Expertise in applying Bayesian statistical methods, machine learning methods, or related statistical inference
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experimental studies, mechanistic modelling, time-resolved data analysis, and machine learning to develop and validate predictive models linking process signals to reaction behaviour, progressing from controlled
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by combining machine learning with in-depth knowledge of biological processes. We aim to work towards foundation models and integrative theories of biological systems, and towards innovative AI-driven