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- 
                Field
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                sub-project; a PhD track, focusses on the characteristics of transformative competencies to be achieved as learning outcomes, and pathways to identify that set, in co-creation with stakeholders 
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                to Alzheimer disease in brain using ultra-high field MRI. This task will involve tailoring contrast agents for MRI at ultra-high fields and linking these agents to nanobodies targeting amyloid deposits 
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                on therapeutically targeting enzyme vulnerabilities. Postdocs will be able to grow and develop their own projects under Dr. Mulvaney’s guidance. The Mulvaney Laboratory within the FBRI and CNRIC is 
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                drug design, protein structure modeling and simulations, target-based and phenotype-based compound screening, single cell data analysis and modeling, medical imaging processing, Natural 
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                tracking and fixed-tilt solar arrays that are both ground-mounted and elevated, creating an incredible space for quantifying the impacts and potential for crop production under a suite of design types 
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                heuristic optimisation to explore and improve materials candidates-especially 2D/vdW structures-for target electronic/spintronic properties. The postdoctoral researcher will lead the optimisation component 
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                to generate novel materials with targeted optical properties. More information can be found in the following News post . Responsibilities and qualifications Your overall focus will be to synthesize and 
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                collaborate with various comprehensive cores at MDACC to develop novel therapeutic strategies that enhance targeted and immunotherapies. Dr. Chen is deeply committed to the professional fulfillment and career 
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                scientific track record demonstrating well-organized design and execution of studies. Experience with various deep etching techniques (e.g., wet etching, RIE). Experience with soft lithography. Experience with 
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                components: Deep learning and LLMs for molecular biology Multi-omics datasets (e.g., TCGA, TARGET, CCLE) Single-cell and spatial transcriptomics/proteomics High-throughput drug and genetic screens (e.g