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service providers. Ability to work independently and collaboratively in a team environment. Ability to work in a high volume, deadline-driven, and results-driven environment. Preferred Skills, Knowledge and
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, physics-based models, and data-driven methods to support design, manufacturing, and decision-making across aluminium value chains. Education and competence building are central pillars of FAST. The centre
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knowledge of AI-enhanced planning in shipbuilding supply chains. Apply quantitative methodologies, such as simulation, analytical modelling, and AI‑driven techniques, to develop decision support for efficient
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of Distributed Mixture-of-Experts (MoE) and Small Language Models (SLMs) to create autonomous, intent-driven networks. This isn't just about connectivity; it’s about building a collaborative, agentic ecosystem
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rates, • Work in a friendly, collaborative and productive environment, • Mentor and teach highly motivated students, • Be part of a mission-driven institution, • Live affordably in a central location with
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sustainability-driven domains (health, transport, energy, manufacturing). Contribute to scientific publications, reports, datasets, open-source tools, and AI model documentation. Support project acquisition by
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environment thanks to its state-of-the-art infrastructure. With an innovative approach, UM6P places research and innovation at the heart of its educational project as a driving force of a business model. About
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Experience Posting Number P260049 Posting Link https://www.ubjobs.buffalo.edu/postings/61494 Employer State Position Type UUP Professional Professional Appointment Term Term Salary Grade SL5 Posting Detail
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employees and contractors. This role balances data driven decision making, stakeholder engagement, and operational excellence to advance institutional priorities, access, safety, sustainability, and customer
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engineering, materials science, or a closely related field, with demonstrated interest or experience at the interface of computation and data driven methods. Experience with computational modelling techniques