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Field
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Requirements Required skills, abilities, and knowledge: Recent or soon-to-be completed PhD (within the last 0-5 years) by the start of the appointment in computer science, electrical engineering, applied
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redox balance. Our studies explore how p53 integrates metabolic cues by acting as both a sensor and regulator of cellular metabolism. In parallel, we are identifying metabolic changes that promote tumor
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associated with biological membranes, including large peripheral membrane complexes and integral membrane transporters. In parallel with mechanistic structural work, we develop targeted chemical probes
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are conducted in parallel in close collaboration with industry, public authorities, and international partners. The successful candidate is expected to contribute to the development of the research, including
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employees, 500 PhD students and 160 technical/administrative employees who are cooperating across disciplines. As a post doc, you will be working at Aarhus University Hospital or another hospital in
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of multiple medicines across cellular mechanisms in parallel, ensuring accelerated assessment of existing and new therapies. Improving our understanding of the cellular basis of disease will help bridge the gap
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validated at CPPM. In parallel, the candidate will improve data reconstruction algorithms by using artificial intelligence techniques (e.g. neural networks), to optimize the separation between signal and
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interest in research focused on the valorization of such by-products. In parallel, consumer demand for clean-label and functional foods, together with increasing restrictions on synthetic additives, has
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to significant losses during processing, transport, retail and storage stages. In parallel, advances in sensor, information and communication technologies, together with increased computational capabilities, have
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candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems (e.g. weather