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Field
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, and spatial transcriptomics. Key responsibilities include: Developing AI/ML methods for image alignment across modalities Automated feature detection Predictive modeling of vascularization patterns
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background in feed processing technologies, biochemical, and chemical evaluation methods. Proven experience in experimental design, data analysis, scientific communication and writing. Demonstrated ability
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project. Your profile We are looking for a highly motivated candidate with a background in machine/deep learning, and communication networks. The required qualifications include: PhD in computer engineering
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highest level of general business knowledge, normally acquired through attainment of a directly job-related terminal degree or equivalent formal training in a recognized field of specialization that is
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resource recovery pipelines You will contribute to the translation of structural and biophysical insights into technologies or methods for transforming recovered biopolymers into valuable products or process
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animal species, generating standardized data that works effectively across diverse languages and cultural contexts while eliminating traditional barriers of recall bias. These methods are being deployed in
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sovereignty, relationality & research development, fieldwork & braiding knowledges, formal & informal science education, storywork & knowledge mobilization, training scientists, and policy & government agencies
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institutions and/or to co-supervise a PhD student. A secondary affiliation to EPFL or UoE may be offered to candidates with the appropriate profile. Appointment is initially for 1 year, and renewable for up to 3
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, Indigenous, and people of color, those with disabilities, those who are first generation, veterans, and those from 2SLGBTQIA+ communities. The postdoc will be formally mentored by Dr. Xiao Zang and will engage
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- PhD student in quantitative verification interested in co-developing Automata Tutor - main developer of Automata Tutor Positions in the Formal Methods for Software Reliability group of TU Munich led by