New

Postdoctoral Research Fellow - Metabolite-Protein Interactions in Neurodegenerative Diseases

Georgetown University
United States, D.C., Washington
May 23, 2025
Postdoctoral Research Fellow - Metabolite-Protein Interactions in Neurodegenerative Diseases Georgetown University: Medical Center: School of Medicine: Biochemistry and Molecular and Cellular Biology Location: Georgetown University Medical Center Open Date: May 19, 2025 Deadline: Jul 25, 2025 at 11:59 PM Eastern Time Description Position Overview The Joshi Laboratory (https://www.joshilab.org/) at Georgetown University Medical Center is seeking a highly motivated Postdoctoral Research Fellow to investigate the complex interplay between metabolite-protein interactions in aging and age-related neurodegenerative diseases. This position explores how metabolites serve as key mediators between environmental exposures and protein structure/function, forming the basis of both metabolostasis and proteostasis during normal and pathological aging. The research aims to decode how age-dependent environmental stressors disrupt these homeostatic systems, particularly focusing on how specific exposome factors accumulate over the lifespan to trigger metabolic changes that drive protein misfolding and aggregation in neurodegenerative diseases. Using cutting-edge structural proteomics, integrated biophysical and biochemical methods, alongside computational approaches, this work will map exposure-metabolism-protein networks to understand fundamental mechanisms underlying the transition from healthy aging to neurodegenerative disease. This position also includes participation in established industry partnerships that will accelerate the translation of research findings into novel therapeutic interventions for aging and age-related neurodegenerative disorders. Duration: Initial 2-year appointment with possibility of extension Salary: Follows NIH Postdoctoral guidelines and comprehensive benefits Start date: as soon as possible Responsibilities Develop and optimize integrated biophysical and biochemical methods to investigate metabolite-protein interactions in aging Apply structural proteomics techniques to capture protein conformational dynamics in patient samples and cerebral organoids Track temporal changes in protein structural modifications and metabolite profiles in response to identified environmental factors Establish cerebral organoid models to study the impact of environmental exposures on metabolostasis and proteostasis Build and validate machine learning models (in collaboration) for molecular data analysis and signature prediction Identify and validate novel molecular signatures specific to biological and environmental changes Design experiments combining biophysical, biochemical, and computational approaches to track how exposome factors alter protein-metabolite interactions Collaborate with industry partners to translate findings into therapeutic strategies Present research at conferences and publish results in high-impact journals Contribute to grant applications, protocol development, mentorship of junior lab members Research Environment The postdoctoral fellow will have access to Georgetown's exceptional research infrastructure, including state-of-the-art Proteomics & Metabolomics facilities equipped with a Thermo Scientific Orbitrap Lumos Tribrid mass spectrometer for high-resolution proteomics, the Microscopy & Imaging Shared Resource featuring confocal, multiphoton, and super-resolution microscopy systems with live cell imaging capabilities, instrumentation at the Institute for Soft Matter Synthesis and Metrology, and a dedicated in-lab confocal Cytation 10 system. The position offers collaborative opportunities across Georgetown's Neuroscience and Neurology departments, Brain Bank, and MedStar clinical network, with mentorship focused on developing scientific independence. Qualifications Required Qualifications PhD in Biochemistry, Biophysics, Chemical Biology, Computational Biology, Molecular and Cellular Biology, Neuroscience, Systems Biology, or related field Strong expertise in biophysical and biochemical methods for studying protein structure and dynamics Experience with mass spectrometry-based methods, including LC-MS, DIA/SWATH-MS, and related proteomics approaches Experience with mammalian cell culture, including iPSCs and cerebral organoids Demonstrated skills in processing and analyzing large-scale molecular datasets Programming skills in Python and/or R Strong publication record in related areas Excellence in experimental design and execution Excellent written and verbal communication skills Strong work ethic and collaborative spirit Desired Skills Experience in machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) Experience with structural proteomics approaches (HDX-MS, LiP-MS, XL-MS) Background in aging research or neurodegenerative disease models Knowledge of exposome research methodologies Familiarity with or desire to learn neural network architectures for biological data analysis Understanding of cloud computing environments (AWS and/or Google Cloud) Application Instructions Interested candidates should submit the following materials through Interfolio: Cover letter describing relevant research experience, including a compelling statement on what draws you to explore the molecular mechanisms of aging and neurodegeneration (your scientific "why") Curriculum vitae including publication list Names and contact information for three professional references PDF copies of representative publications For more information and to apply, visit https://apply.interfolio.com/166189 GU is an Equal Opportunity Employer. All qualified applicants are encouraged to apply, and will receive consideration for employment without regard to age, citizenship, color, disability, family responsibilities, gender identity and expression, genetic information, marital status, matriculation, national origin, race, religion, personal appearance, political affiliation, sex, sexual orientation, veteran status, or any other characteristic protected by law. jeid-715239ecd6f3d541a2074cfb3d0eb3ff