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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 5 days ago
. Preference will be given to candidates with aptitude for working in disaster risk management environments supported with GIS and Machine Learning, basic programming skills (e.g., Python, R, or similar
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for any reason, throughout the course of employment. Learn more about the work modes . Background Screening The University of Michigan conducts background checks on all job candidates upon acceptance
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Location_ONB: Kent Ridge Campus Posting Start Date: 31/10/2025 Job Description The successful candidate will work with Associate Professor Duane Loh on conducting research at the interface of Machine Learning
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the use of modern machine‑learning methods within applied mathematics—particularly physics‑informed learning, anomaly detection, data‑driven modelling, and the construction of surrogate models grounded in
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. The appointment is for three years with research duties exclusively. A career plan will be prepared that specifies the competencies that the Research Fellow will acquire. Access to career guidance will be provided
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ability to purchase additional leave; flexible working arrangements to help manage your work-life balance; ongoing learning and development opportunities to grow your career; an inclusive and supportive
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and
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surroundings, strong community, and modern learning and research facilities. This appointment is for a fixed term of three years. Mōu - About you The successful candidate will have completed a PhD in industrial
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and wave-equation–based modeling, including familiarity with adjoint-state methods, gradient-based optimization, and multi-scale inversion strategies. Proven expertise in machine learning and deep
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avian influenza (HPAI) airborne transmission between U.S. poultry facilities. The primary focus of this opportunity will be learning to develop statistical and mathematical models to assess airborne