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Researcher (R2) Positions Postdoc Positions Country Poland Application Deadline 1 Jan 2026 - 23:42 (Europe/Warsaw) Type of Contract Temporary Job Status Full-time Hours Per Week 40 Offer Starting Date 1 Feb
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in international research visits if needed. We are looking for a highly motivated researcher with: A PhD in machine learning, computer vision, remote sensing, glaciology, climate science, or a related
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economic assessments machine learning or proxy-model based methods field scale simulation geological features geomechanics reactive flow The PhD fellow are not expected to master all these topics. Project
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. Familiarity with machine learning (ML) techniques for optimizing models or analyzing complex datasets. Experience with space-based instrumentation or similar high-precision systems. Demonstrated expertise in
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, +4687906000 Published: 2025-11-06 Last application date: 2025-11-15 Where to apply Website https://academicpositions.com/ad/kth-royal-institute-of-technology/2025/postdoc… Requirements Research FieldComputer
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, residents, postdocs, and fellows. We aim to recruit, support, retain, and promote diversity in our department. For more information on our department, please see our website: https://med.stanford.edu
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journey toward the future of medicine and Precision Health. Stanford is rooted in a culture of excellence and values innovation, collaboration, and life-long learning. To foster the talents and aspirations
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, Analytical Thinking, Computerized Accounting, Computer Literacy, Deadline Management, Grant Administration, IBM Cognos Analytics, Interpersonal Communication, Learning New Technologies, Learning Systems
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are proficiency in Python, deep learning frameworks such as Keras, PyTorch, TensorFlow, or other related software stacks, and a solid background in machine learning, cybersecurity, or AI robotics, and the ability
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of faculty at SUNY Polytechnic Institute and the University of South Florida, consisting of mathematicians, physicists, computer scientists, and engineers investigating applications of deep learning