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relevant field (e.g. computer science, artificial intelligence, machine learning, computer vision, animal science, biology, veterinary medicine, or a related discipline) have documented experience in
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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multi-modal perception and machine learning. Current noninvasive agricultural monitoring systems rely primarily on passive sensing, which limits sensitivity to early-stage plant stress. This project
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The applicant must: hold a PhD in a relevant field (e.g. computer science, artificial intelligence, machine learning, computer vision, animal science, biology, veterinary medicine, or a related discipline) have
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. The position bridges machine learning and molecular science, with opportunities for collaboration, mentorship, and impactful research. About us The Department of Computer Science and Engineering (CSE
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science, machine learning, automated systems, or a closely related field Have experience working with ruminants Have experience in precision agriculture and/or precision livestock farming Have experience
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statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft matter and nanomaterials
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society in transition is key. We also take pride in delivering education to enable regions to expand quickly and sustainably. In fact, the future is made here. Are you interested in learning more? Read here
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network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce using UPSC transformation and somatic embryogenesis pipelines; evaluating drought
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loop/TAD structures. - Perform comparative analyses versus Populus tremula; apply network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce