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Mathjobs.org | 26 days ago
discovery: AI-assisted simulation, surrogate modeling, inverse learning problems, automated scientific discovery, generative approaches for science, and data-driven discovery of mathematical or physical
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discovery: AI-assisted simulation, surrogate modeling, inverse learning problems, automated scientific discovery, generative approaches for science, and data-driven discovery of mathematical or physical
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, Applied Mathematics, or a related field. Strong expertise in: Computer vision and image processing Deep learning (e.g., PyTorch, TensorFlow) Data-driven modeling and analysis Proficiency in Python and
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Researchers in AI-driven atomistic modeling and AI-accelerated cheminformatics to join the Otaniemi Center for Atomic-scale Materials Modeling (OCAMM), hosted by the Department of Chemistry and Materials
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engineering, materials science, polymer science, physics, computer science, and/or data science Demonstrated accomplishments in materials informatics, scientific machine learning, or AI-guided
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, data science, applied mathematics, or a closely related field Demonstrated experience with multimodal data acquisition, data analytics, statistical modeling, and machine learning in manufacturing
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, and LPBF Strong background in materials science, including characterization techniques such as SEM , TEM , FIB , XRD , nano-indentation, etc. Experience with analytical/numerical thermal, thermo
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, and LPBF Strong background in materials science, including characterization techniques such as SEM , TEM , FIB , XRD , nano-indentation, etc. Experience with analytical/numerical thermal, thermo
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backgrounds in biomedical informatics, computer science, machine learning, statistics, data science, computational biology, or related quantitative disciplines. Experience with longitudinal health
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and dissemination efforts. Perform other related duties as assigned. Qualifications: A Ph.D. degree (by early 2027) in computer science, data science, statistics, bioinformatics, electrical