73 machine-learning-"https:" "https:" "https:" "https:" "https:" research jobs in Sweden
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- Chalmers University of Technology
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of working with motion capture, eye tracking, machine learning, or other advanced behavioral analyses or related research experiences. A consistently excellent academic track record is required, including
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multi-modal perception and machine learning. Current noninvasive agricultural monitoring systems rely primarily on passive sensing, which limits sensitivity to early-stage plant stress. This project
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statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft matter and nanomaterials
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models for complex data, including temporal data
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testing and collaboration with infrastructure owners or managers - Experience in supervision - Knowledge of data-driven methods, signal processing, or machine learning - Familiarity with sustainable
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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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, and demonstrated ability to develop computational pipelines for biological datasets. Experience in statistical modeling and/or machine learning applied to biological systems, with the ability to link
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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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. The position bridges machine learning and molecular science, with opportunities for collaboration, mentorship, and impactful research. About us The Department of Computer Science and Engineering (CSE
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. Lead and conduct research projects in data-driven nutrition, such as: statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and health data analysis of omics data