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, Molecular Tools and Functional Genomics, Neuro-Oncology and Neurodegeneration and Vascular Biology. Departmental activities are also integrated with the units for Oncology, Clinical Genetics, Clinical
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | 2 months ago
link: https://www.ncbi.nlm.nih.gov/myncbi/nathan.lawson.1/bibliography/public/ Send related inquiries to: Nathan D. Lawson, Ph.D. Professor Department of Molecular, Cell, and Cancer Biology University
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. Familiarity in bio/nanomaterial synthesis, bioimaging, drug delivery, radiochemistry, and/or molecular biology techniques is preferred. Experience working with radioisotopes and small animal models is desirable
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dedicated to elucidating the cellular and molecular mechanisms that establish immune tolerance to the microbiota, exploring how dysregulation of these pathways contributes to diseases and disorders in the gut
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– Molecular-Scale Modelling This PhD position focuses on molecular-scale computational modelling of polymer-hybrid anion-exchange membranes. The project involves DFT, molecular dynamics, and Monte Carlo
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chemistry and compound purification. Knowledge of molecular design and molecular modeling tools (e.g. QSAR models, pharmacophores, docking, molecular dynamics). A high level of motivation and interest in
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dynamics and biomolecular condensates contribute to PD co-pathologies in human midbrain assembloid models. The work combines advanced imaging, molecular biology, and functional disease modeling. Key
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behavior of pests, their response to pesticides, including physiological and genetic mechanisms of resistance, innovations in applied pest management research (e.g., molecular, computational modeling
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, including data processing and analysis through chemometric modelling. It will also be considered as a preferential factor: a. Experience in PHA characterization, including chemical, physical, and molecular
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characterizing the epigenetic landscape of DM-associated genes and mapping their regulatory elements in cellular models using genomic methods (e.g., ATAC-seq, CUT&RUN, Capture-C, Micro-C). Next, in collaboration