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describing their qualifications and relevant experience to Gideon Bradburd ([email protected]). Job Summary We are currently seeking applicants for an NIH-funded postdoc position in the Bradburd lab in
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. Desirable criteria Experience with training or fine-tuning LLMs or other generative AI models Excellent programming skills and familiarity with modern AI frameworks Downloading a copy of our Job Description
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robust management of large traffic networks under all conditions. You have the most important role in this ambitious project as one of the young talents in our team. We have 2 PhD and one postdoc positions
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to develop novel AI and machine-learning methods for accelerated materials discovery. The focus is on combining generative AI, active learning, first-principles simulations, and machine-learning potentials
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
methodologies, such as simulation, analytical modelling, and AI‑driven techniques, to develop decision support for efficient planning and coordination of production activities in supply chains and generate
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electromagnetic simulation tools, such as CST Studio Suite or Ansys HFSS, and you use them systematically as part of your design workflow. You also have practical skills in numerical modelling and data analysis
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The postdoc project aims at fabricating graphene edge sensors for single molecule detection and biopolymer sequencing. The device architecture will be scalable and tunable by combining unique
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use simulations, models, and analysis frameworks to support detector design, performance characterization, and interpretation of experimental results. Prepare research results for publication in peer
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Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Application Deadline 17 Aug 2026 - 23:59 (Europe/Andorra) Country Spain Type of Contract Temporary Job Status Full-time Hours Per Week
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PostDocs on the development of cutting-edge AI-ready/agentic infrastructure for data and model discovery, model metadata extraction, and scholarly intelligence. The project will explore conceptual frameworks