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partners. The main goal of the project is to decisively improve the understanding of GIA-driven processes and, consequently, the GIA modelling in Antarctica. Your responsibilities: Analysis, processing and
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of microfluidic cultivation platforms for co-cultures Live-cell microscopy (including fluorescence, phase contrast and time-lapse) for the analysis of cell interactions. Conducting cultivation experiments with
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: A completed university degree (Master or equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field Prior experience in computer vision, deep
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profile in geo, environmental, energy, and material sciences. In this environment, we are a strongly mathematically oriented research group with a focus on the analysis of various problems in geophysics (in
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, Regional Science, Economic/Quantitative Geography, or Mathematics knowledge and experience in spatial equilibrium modeling (e.g., through coursework, thesis work, or previous projects) is beneficial
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experimental molecular biology and data analysis. Doctoral candidates can specialize in genomic and molecular biology techniques, as well as in algorithms, statistics, and artificial intelligence for molecular
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equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field Prior experience in computer vision, deep learning, or signal processing; familiarity with
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Analysis of Microscopic BIOMedical Images (AMBIOM) You will be responsible for Developing new machine learning algorithms for microscopy image
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Master’s degree (or equivalent) in mathematics, computer science, physics, or related field. Sound knowledge in (scientific) machine learning, and knowledge in numerical analysis and numerical linear algebra
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at the Faculty of Mathematics at TUD. Tasks: generation of hyper uniform patterns (point, scalar and vector fields) application of topological data analysis tools such as persistent homology and graph statistics