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Flow Cytometry Expert (Human vs non-human Data Alignment) Goal: Diagnose and fix why human vs non-human flow cytometry data separate into different clusters after compensation/normalization/embedding. Align equivalent populations (CD4, CD8, B, NK) across species. Data & Stack: FCS + FlowJo workspaces; existing Python/FlowKit pipeline using MNN, UMAP, Leiden/PhenoGraph. Tasks: Review compensation & transforms, marker harmonization, batch correction, and clustering. Propose and implement fixes to improve cross-species alignment. Deliverables: Brief diagnostic summary, corrected code/pipeline, and before–after cluster/UMAP comparisons. Skills Needed: Flow cytometry, FlowJo/FlowKit, batch correction (MNN), Python (scanpy/umap/leiden), strong QC mindset. Experience with cross-sample or cross-species FCM integration would be prefered. To apply: When applying, please include real examples of similar work you’ve done and explain your debugging approach. *Important*: Please start your proposal with the word "Human-NH" to make sure you read the description and are a real applicant.