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to biocuration, semantic interoperability, ontology-aware data integration and the development of structured evidence resources Bioinformatics and mechanistic data integration Analyse and integrate high
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collegial and collaborative interdisciplinary scientific group, with strengths in neuroscience, cancer research, structural biology, epigenetics, cellular optogenetics, glial biology, and bioinformatics. We
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The Savinov lab at UT Austin (https://www.savinovlab.com/ ) is pursuing multiple projects centered around discovering and designing protein fragments as universal regulators of protein interactions in health
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responses. Structural Bioinformatics & Biophysical Modeling: Protein structure prediction (e.g., AlphaFold, RoseTTAFold, ESMFold), molecular dynamics (MD) simulations, or protein-protein/antigen-antibody
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-funded project, subject to contract negotiations. You will develop and apply machine-learning and structural-bioinformatics methods for protein design, with a particular focus on modern generative
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(e.g., scRNA-seq) to investigate cellular heterogeneity, host-pathogen interactions, and immune responses. Structural Bioinformatics & Biophysical Modeling: Protein structure prediction (e.g., AlphaFold
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experimental evolution, microbial ecology, bioinformatics and quantitative analysis. We combine experimental microbiology with data-driven approaches and collaborate with national and international research
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development Qualifications PhD degree in Bioinformatics, Computational Biology, Computer Science, Mathematics, Physics, or a related field Strong experience with programming in Python, R, or similar languages
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genes involved in siderophore synthesis or transport and the construction of reporter strains (e.g., fluorescent genes) to track in situ siderophore production. - Process and analyze experimental data
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https