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biological systems, also in collaboration with other researchers and companies. Your profile Applicants should hold a PhD in Computer Science, Computer Engineering, Artificial Intelligence, Physics
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requirements A PhD in a relevant subject such as mathematics, computer science, physics, engineering, or a related discipline. Candidates who have not yet defended their PhD are eligible to apply, provided
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, probabilistic models Representation learning, self-supervised learning, foundation models Data analysis, non-linear statistics, knowledge management Your profile PhD in Computer Science, Bioinformatics
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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and Fluidigm technologies at UTHSC. Qualifications PhD in Computer Science, Electrical Engineering, Biomedical Engineering, Statistics, or a related field. Strong background in machine learning, data
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), including research project supervision and teaching of research skills. What do you have to offer A PhD in neuroscience, psychology, computer science, or a related field; Peer-reviewed publications based
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methods (e.g., PCA, PLS-DA, clustering, neural networks) to enable automated, polymer-specific classification. Optimize workflows for high-throughput imaging and real-world sample variability, minimizing
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electrophysiological and calcium imaging recording techniques to assess glia and neural activities. Perform pharmacological modulation to investigate neural circuit activity in the context of metabolism and energy
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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Learning (DL) tools tailored specifically to the particularities of TCR interactions. As part of the Deep Immune Receptor Modeling (DIRM) grant from the NNF Data Science Collaborative Research Programme