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learning - deep neural network, recurrent neural network, LSTM Optimization theory Stochastic geometry Strong programming skills in at least one of the following: MATLAB, Python, JULIA or C++ Language
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nutrients with the soil through adaptive root and fungal networks. The successful candidate will design and implement a modelling framework based on Partial Differential Equations (PDEs) to represent
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-informed / simulation-aware modeling Efficient algorithms for design-space exploration (e.g., surrogate modeling, Bayesian optimization, differentiable programming) Hybrid approaches combining data-driven
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based on Partial Differential Equations (PDEs) to represent the coupled dynamics of roots, mycorrhizal fungi and soil resources under varying environmental conditions. The work will integrate concepts
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Differentiate iPSCs into midbrain dopaminergic neurons and organoids for phenotyping and compound testing High content imaging and automated image-analysis Analyze omics data Collaborate closely with
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, differentiation therapy and mild magnetic hyperthermia- CUP: J53C23003330007 Please submit your application using the online form and including a detailed CV, university transcripts, cover letter, and contact
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, when teaching courses on contemporary social theories and research on discourse and/or social inequalities and class differentiation, will be able to explain key concepts and their application
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responsibilities: Differentiation and characterization of iPSC-derived midbrain assembloids Analysis of stress granules and membraneless organelles using imaging Application of stress paradigms and assessment
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(transient or stable expression of cDNA/shRNAs/CRISPR, mutagenesis, IPs, immunoblotting, nucleic acid extractions, PCR, phenotype-based cell assays including proliferation, apoptosis, myogenic differentiation
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-on-chip systems to model metabolic interactions; Implementation of cell cultures and differentiation of iPSCs; Processing of histological samples for optical and electron microscopy; Analysis