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. The postdoctoral researcher will: - design and conduct molecular, cellular, biochemical, and biophysical experiments; - investigate protein interactions mediated by intrinsically disordered regions and short linear
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Candidates (DCs) of the MSCA Doctoral Network AEGIS — Adaptive Grid-Interactive Edge Datacenter Fleets . Marie Skłodowska-Curie fellowships are among the most respected and best-paid PhD positions in Europe
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
Will Gain from This PhD This PhD offers the opportunity to: Develop highly sought-after skills in knowledge engineering, semantics alignment, and collaborative innovation. Collaborate with leading
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shifted away from what was once validated. The PhD addresses both. On the interaction side, it studies how an agent calibrates its own initiative so that it is helpful rather than intrusive, how it presents
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insurance, generous paid leave and retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Position Description Advertised
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quantitative microscopy. Ideal Candidate Qualifications: Candidates must have recently obtained (or will obtain before starting) a PhD from a biomedical research background. Experience in one or more areas of
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, artificial intelligence, food technology and sustainability from KU Leuven, ILVO and Artevelde University College of Applied Sciences. The PhD candidate will be responsible for:•Designing and developing
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, such as indirect interactions between organisms, population viability analysis, and nature reserve design and management, will also be discussed. Lectures will cover theoretical concepts in individual
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a manuscript to a scholarly journal Make a paper presentation at a professional meeting Co-develop a practitioner-oriented output (i.e., related to a decision framework) Interact with faculty, PhD
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design spaces, long-duration simulations, and real-time applications. This PhD project will develop physics-informed deep learning and surrogate modelling approaches to accelerate simulation, uncertainty