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analysis and biomedical data analysis, with demonstrated experience in organ segmentation from medical images, using both traditional and machine learning–based methods, and creation of large segmentation
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machine-learning tools. Data analyzed include precursors such as volatile organic compounds, aerosol number and mass concentrations, chemistry, biological particles, cloud and ice condensation nuclei, light
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The applicant must: hold a PhD in a relevant field (e.g. computer science, artificial intelligence, machine learning, computer vision, animal science, biology, veterinary medicine, or a related discipline) have
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practices” research themes. The successful candidate will have: a PhD in Translation Studies/Machine Translation; practical experience conducting data-driven research in a machine translation/large language
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Functional Theory (DFT), machine-learned force fields (MLFF), graph neural networks (GNNs), or large language models (LLMs). Extensive Knowledge In: • First-principles atomistic simulations with packages
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applied machine learning. Location: SustAInLivWork Centre of Excellence (CoE) (Artificial Intelligence Centre of Excellence at Kaunas University of Technology (KTU)), Kaunas, Lithuania. The role requires a
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https://pubs.acs.org/doi/full/10.1021/acssuschemeng.5c0419 The successful candidate will be able to: Work safely and independently in a laboratory setting Learn new techniques and protocols Plan and
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computational focus on innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. The subject area concerns
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» Computer engineering Computer science Mathematics Engineering » Mechanical engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 27 Mar 2026 - 17:00 (Europe
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perspective, including (but not limited to): machine learning and statistical learning computer vision and sensor-based data analysis natural language processing and large language models hybrid, interpretable