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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
include preparing research protocols, collaborating with MIT faculty and project investigators on strain evaluation, model development, and interpretation of findings, and contributing to data-driven
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18 Sep 2026 Job Information Organisation/Company KU LEUVEN Research Field Computer science » Informatics Engineering » Biomedical engineering Engineering » Computer engineering Biological sciences
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using Large Language Model (LLM) APIs (e.g., Gemini, OpenAI, Claude) and prompt engineering techniques. AI Agent Frameworks: Hands-on experience working with modern AI agent frameworks (e.g., LangGraph
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Modeling, and an online M.S. in Data Science offered in collaboration with the Department of Computer Science. SDS is internationally recognized for excellence in statistical methodology and theory, applied
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, access to industry-driven case studies, real-world data, and state-of-the-art modelling tools. For the Project Researcher position, the successful candidate will be expected to apply for and pursue a
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for entry into a PhD program. A background in machine learning, inverse problems, scientific computing, or related data-driven methods is highly desirable. You are curious about combining physical modeling
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Systems Engineering, driving forward computer-aided and experimental approaches to the modelling, simulation, analysis and optimisation of sustainable process engineering processes, incorporating
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extracellular matrix biology, biomaterials, hydrogel systems, 3D culture, tissue engineering, or disease-relevant cell–matrix models. About the School The School of Physical and Chemical Sciences (SPCS) at Queen
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in high-tech, medical, and energy systems. Information Modern engineering increasingly relies on data-driven models to describe complex dynamical systems. Scientific machine learning is now enabling a
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, including emerging life-sciences foundation models, to investigate cancer aetiology, identify biological and clinical subtypes, and develop robust models of cancer risk that can inform prevention, screening