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, from September 2025 a Postdoctoral Researcher – Longitudinal Proteomic Analysis (Wet Lab & Data Science) Reference number: 2025-0174 The Division of Medical Physics in Radiation Oncology develops new
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, behavioural analysis, investigations of ALS human post-mortem tissue, spatial transcriptomics, and gene therapy. The successful applicant will have completed a PhD in Neuroscience with expertise in molecular
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Postdoc in "Navigating uncertainty: Planning marine protected areas in a changing Southern Ocean"...
statistics and the ability to apply quantitative analysis to ecological data A strong background in programming (preferably in R), including data manipulation, statistical analysis, and spatial modelling and
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emissions due to the large temporal and spatial variations in CH4 emission and its underlying processes. The lack of continuous observations of CH4 turnover hinders our understanding of CH4 emission
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in physical layer algorithm design and performance analysis Proven track record of publications in relevant IEEE journals and conferences In addition to the above qualifications, it is highly desirable
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technologies (e.g., spatial transcriptomics, CODEX, Akoya PhenoCycler). Proficiency in single-cell RNA sequencing, high-dimensional data analysis, bioinformatics tools, and programming languages (R, Python
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behavior. Preferred Qualifications: Experience using quantitative spatial techniques to characterize neuron distribution. Brief Description of Duties: The Postdoctoral Associate will assist the Principal
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of the Mississippi embayment), surface water hydrology and modeling, model coupling, and spatial/temporal data analysis. The successful candidate will work with Dr. Brian Waldron and CAESER faculty/staff on projects
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crust and mantle lithosphere. This covers the entire spectrum of topics ranging from geomechanics and rock physics to the rheology of crust and uppermost mantle, ranging over the entire spatial bandwidth
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. Required Qualifications: A doctoral degree (PhD, MD, or equivalent) conferred by the start date. Proficiency in R/Python Experience with scRNAseq, and/or spatial proteomic/transcriptomic data analysis Growth