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Field
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requiring AI are not necessarily permanently connected to the grid. The need to develop an energy-efficient hardware for the implantation of AI in nomadic systems is becoming increasingly urgent. Major
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Intelligence (AI) and machine learning (ML) techniques. You will develop AI-based predictive models to anticipate user engagement, primarily using data collected through unobtrusive measurements (e.g., websites
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in Austria. We are seeking a PhD candidate on predicting adherence to digital health-promoting interventions with Artificial Intelligence (AI) and machine learning (ML) techniques. You will develop AI
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), ecological processes (primary productivity and decomposition rates), and greenhouse gases (3D-printed flux chambers), and investigating how we can use citizen science-driven data streams for model development
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development activities and all other scientific and technical activities within the scope of the missions of the respective Organic Unit and of U.Porto regularly [Article 7(2) of Regulation 487/2020 of 22 May
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University, you will develop innovative methods to make geographic data smarter and more question-aware, contributing directly to the future of spatial reasoning and sustainability. Your job Answering
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research together with cutting-edge materials science and physics. Depending on your background you will work collaboratively on the following tasks with either with a stronger model-development or
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the drafting of clinical studies including defining goals and objectives, milestones for completion and developing budgets for support. Assists in drafting Institution Review Board submissions. This includes
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | about 2 months ago
related to staff position within a Research Infrastructure? No Offer Description The Quantitative Genetics research group is interested in developing statistical genomics toolboxes to decipher the genetic
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aerodynamics, gust interactions, and fluid-structure interactions. The research focuses on unfolding the origin and development of unsteady flow separation and vortex formation. The lab has built a reputation