13 phd-in-computational-mechanics-"Prof"-"Prof" Postdoctoral positions at Emory University in United States
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prostate cancer; 2) Deciphering the epigenetic mechanisms of oncogenic gene upregulation driven by non-canonical promoters; 3) Discovery of novel druggable regulators of neuroendocrine surface protein target
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: The qualified candidate should hold a PhD in Medicinal Chemistry, Radiochemistry, Neuroscience, Pharmacology, or related fields. Hands-on experience with CNS models (e.g., neurodegenerative or psychiatric models
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QUALIFICATIONS: - PhD in Ecology and Evolution, Virology, Bioinformatics, Computer Science or a related discipline - Demonstrated interest in computational analysis of biological data - Advanced coding skills
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, computational, and machine learning/AI methods, with a particular emphasis on deep learning approaches improve our understanding and prediction of infectious disease dynamics. Projects are also strongly grounded
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qualifications as specified by the Principal Investigator. PREFERRED QUALIFICATION: PhD. NOTE: Position tasks are generally required to be performed in-person at an Emory University location. Remote work from
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human cancer based on their cancer driver gene mutations. A new project in the lab is aimed at understanding the mechanism of natural killer mediated anti-tumor effects in LKB1-mutant lung adenocarcinoma
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and Skills 1. A PhD, ScD, PharmD, MD, or equivalent degree, with experience in clinical research, epidemiology, pharmacology, or related fields 2. Strong background in epidemiological/statistical
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, including super-resolution microscopy, and correlative light-cryo-electron microscopy (cryo-CLEM), to elucidate key steps of viral life cycle and the mechanism of virus restriction by host factors. Highly
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infection and explore strategies to enhance mucosal immunity. Using a combination of in vivo animal models and ex vivo 3D cellular systems, we study airway-specific immune mechanisms that influence
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language models (LLMs), generative AI, and cancer informatics. Projects may include training and fine-tuning domain-specific LLMs for clinical document summarization, treatment recommendation reasoning