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Field
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This PhD project, part of the REACT MSCA Doctoral Network, aims to develop an energy-efficient compute-in-memory (CIM) architecture using gain-cell memory for real-time edge learning, addressing
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improved using machine learning techniques. The developed techniques will be applied to metrology of semiconductor samples. Job requirements You are an enthusiastic candidates with a ‘drive’ for applied
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and eager to acquire the relevant techniques over the course of the PhD. Solid working knowledge of cell wall integrity signalling in plants. Experience in RNA isolation from diverse organisms and
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peptides and nanobodies using diagnostic and therapeutic radionuclides. Perform quality control and analytical characterization of newly developed radiopharmaceuticals. Conduct cell-based binding, uptake
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of biophysics, non-equilibrium statistical physics, and cancer biology. Its objective is to understand how stochastic fluctuations and cell-cell interactions combine to drive the spontaneous emergence
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, socially engaged researchers and enabling impactful research with embedded global mobility opportunities. The Scheme currently consists of two streams; however, the structure and funding opportunities may
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adipose tissue-derived secreted factors and extracellular vesicles influence cardiomyocyte metabolism and contractility, and on validating newly identified biomarkers using human stem cell-derived
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design and evaluate immersive XR experiences that allow users to safely acquire navigation skills in realistic environments such as public transportation hubs, university campuses, and airports. You will
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design and evaluate immersive XR experiences that allow users to safely acquire navigation skills in realistic environments such as public transportation hubs, university campuses, and airports. You will
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advanced machine learning models for the stratification and diagnosis of ANOCA; Provide cell-type–resolved and pathway-level mechanistic insights into ANOCA pathophysiology through multi-omics analyses