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refinement. Publish results in peer-reviewed journals and present findings at international conferences Required Qualifications PhD in proteomics, analytical chemistry, physical chemistry, or a closely related
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to hold (or be close to completing) a PhD in political science or another field relevant to the project. Applicants from various disciplines, e.g., Political Science, Communications and media studies, Data
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deliverables Support supervision of MSc and PhD students related to the project Qualifications You must have a PhD degree in robotics, mechatronics, control, or a related field. We expect you to have: Solid
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polar orbit, passing near the poles about 15 times per day and regularly observing the CIFAR study region. Its payload - two optical cameras, a thermal camera, and onboard machine-learning capabilities
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knowledge of impurity effects or gas-phase contamination in electrochemical energy conversion systems As a formal qualification, you must hold a PhD degree (or equivalent) in electrochemistry, materials
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is expected to hold a PhD degree relevant to the topics of the fellowship. Such a degree might be in (Medical) Sociology, Public Health, Epidemiology, or another area related to survey data analysis
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key agroecosystem variables. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and
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energy systems modelers to supply validated process data for system-level energy and carbon footprint modeling. Supervise and mentor MSc and PhD students contributing to process analysis, instrumentation
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with two PhD students appointed across Department of Architecture, Design & Media Technology and Department of Sustainability & Planning. The position further offers engagement in a broader research
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). We seek an outstanding, ambitious, and highly motivated candidate with a PhD degree in chemistry (or equivalent), preferably with expertise in physical chemistry, theoretical chemistry or computational