1,022 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"ISCTE-IUL" Fellowship positions
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including functional enrichment (GO, KEGG), network analysis, genome assembly and binning, systems biology, and multi-omics integration. Apply statistical modelling, machine learning, and deep learning
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: PhD degree in Computer Science, Electrical Engineering, or a closely related field Strong research background in computer vision and deep learning Solid experience with multimodal learning, segmentation
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learning based optimization algorithms, human and AI coordination in best decision making for urban transportation related problems. The role will focus on developing generic frameworks and innovative
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successful candidate, you will: This project with Dr. Nagarajan Vaidehi involves developing and application of interpretable machine learning methods to uncover allosteric regulation of disordered regions in
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, alignment, evaluation). Design multi-MLLM collaboration methods (knowledge transfer/distillation, federated learning). Build efficient training/benchmark pipelines and report results with clear metrics. Apply
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analysis, PDE-constrained optimization and optimal control, numerical analysis and scientific computing risk-averse and fractional models, digital twins and data-assimilating models, machine learning and
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Responsibilities: Conduct research in Systems Biology to study cell signaling Perform large-scale signaling analysis or protein-interactome analysis Utilize machine learning-based biological data mining and analysis
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Qualifications Experience with machine learning and AI models. Ability to communicate clearly with colleagues, Data presentations Scientific publication experience Special Instructions Priority Application Review
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Machine Learning (OxCSML) research group, with responsibility for leading and carrying out research pertinent to the project, as well as day-to-day management of research activities relevant to the project
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research. About the role: We are seeking a data scientist with extensive expertise in statistics, machine learning and AI, and with experience in mental health research and the analysis of electronic health