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neurodegenerative processes. Responsibilities: Conduct biochemical, genetic, and transcriptomic analyses of key regulators of brain aging using mouse and cerebral organoid models. Design and perform experiments
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position from the date of hire, renewable on an annual basis, contingent upon satisfactory work performance, research program needs, and continued availability of funding. Extensions are common and expected
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one year with a possibility of renewal based on performance. Basic Qualifications Candidates must have a PhD in physics, biology, or a related field by the time of appointment. The ideal candidate will
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. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a strong background in this area, as well as a genuine interest in continuing such work
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the possibility of extension, contingent upon work performance, business need, and continued funding to support the position. Salary and Benefits: This position is salaried and benefits eligible. Information
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./Ph.D. in Biology, Biophysics, Biomedicine, or a related field Demonstrated experience working with and imaging mouse models Experience harvesting tissues and performing in vivo and ex vivo tissue imaging
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settings · Collect and synthesize environmental data, contextual observations, and metadata · Perform statistical and qualitative analysis to identify VOC exposure patterns and chemical signatures · Generate
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analysis and research dissemination, working closely with the project PI (Dr. Desai). The appointment is for one year with a possibility of renewal based on performance. Basic Qualifications: Candidates must
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and Applied Sciences Department/Area Electrical Engineering/Computer Engineering/Computer Science Position Description Project Deep learning plays an essential role in the operation of an autonomous
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interatomic potential to incorporate materials response . Resulting models are implemented in LAMMPS and are used to perform ML-accelerated large-scale dynamics simulations to investigate the evolution