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, Materials Science, Engineering Mechanics, Manufacturing Engineering, Mechanical Engineering, Artificial Intelligence/Machine Learning, or a related field completed within the last 5 years Preferred
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Center for Biologics Evaluation and Research (CBER) | Southern Md Facility, Maryland | United States | 6 days ago
with advanced machine learning approaches. The research will involve assay development, metagenomic (mNGS) sequencing, and bioinformatics analysis to advance regulatory science and enhance the safety
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economic modeling, with interests including improved spatial resolution and machine-learning-enabled approaches for policy analysis. Postdoctoral Position (f/m/d) – Integrated Assessment Modeling (Climate
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of topological and geometric machine learning? Join the CIRCLE group at Imperial College London, led by Dr. Tolga Birdal, in a fully funded postdoctoral research role to lead transformative projects spanning
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expert prototyping, technical consultation, precision fabrication, and instrument repair. Equipped with advanced manufacturing tools, the shop fosters a culture of craftsmanship and continuous learning
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. Required PhD in Computer Science / AI / Machine Learning Strong publication record in AI, ML systems, or related areas Strong programming skills in Python, C/C++ and experience with PyTorch, TensorFlow, JAX
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at CERI under supervision of Dr. Goebel. and should contribute broadly to induced seismicity research. Specific research topics include seismicity analysis, machine learning, reservoir modeling, geothermal
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scanning and Time-of-Flight (ToF) sensors, to enable robust material identification directly in non-laboratory, real-world environments. The acquired data will be processed using advanced machine learning
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Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Application Deadline 15 May 2026 - 23:59 (Europe/Vienna) Country Austria Type of Contract Temporary Job Status Negotiable Is the job
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and/or R statistical programs and web programming. Strong computer skills. Ability to make initial decisions about issues with participants that would cause them to not be able to complete a research