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Field
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biostatistics and machine learning, with particular emphasis on robust and trustworthy methods for learning from complex biomedical data. The postdoctoral associate will join an active and collaborative research
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, and related techniques Conduct electrochemical testing and benchmarking; analyze and interpret complex datasets to elucidate mechanisms and structure–property relationships Document results and lead
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and professionally within a senior research team structure. Problem-Solving: Demonstrated strong problem-solving skills and an ability to independently address complex research challenges. Ability
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processes Experience with the application of statistical methods, data analytics, or machine learning to enrich experimental or computational data sources Experience with visualization of complex 3D flow
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associate to develop deep learning algorithms: 1) to model 3D protein complex structures and impact of mutations/PTMs on protein structure and interactions; and 2) to dissect functional elements (e.g
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principles for these systems. Duties will include conducting computational research work centered on developing new computational methods for simulating complex chemical and physical systems; leading
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regulation by integrating crosslinking mass spectrometry, cryo-electron microscopy, and structural modeling. The project seeks to map how protein complexes exist in their native cellular environments a central
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synchrotrons and x-ray free-electron lasers. Key Responsibilities Perform electronic-structure calculations using ab initio quantum chemistry methods and software (commonly CASSCF-based approaches) Investigate a
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the binding locations of various proteins within the nuclear pore complex (NPC), the locations of various transport pathways though the pore, and the structural and dynamic properties of the FG
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harness the nonequilibrium correlation between structural, charge, and spin/pseudospin degrees of freedom in two-dimensional (2D) materials. The success of this program will lead to new means to control