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Field
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Information Eligibility criteria Applicants should hold a PhD in theoretical chemistry, physics, materials science, or a related field; -demonstrate strong expertise in machine learning (regression, neural
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designing/programming experiments, recruiting/running participants, developing and using computational modeling approaches (Bayesian, RL, neural networks) to analyze behavioral and neuroimaging data
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
immunotherapies, integrating graph neural networks, regulon-aware pooling, and transfer learning with biological regulatory networks. 4) Developing and validating computational biomarkers (IGR burden, TAA burden
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Rutgers University–New Brunswick and collaborating university wide, RBHS includes eight schools, a behavioral health network, and five centers and institutes that focus on cancer treatment and research
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Graph neural networks Generative models (VAEs, diffusion models) Multi-modal learning Knowledge of bioinformatics tools and data formats (FASTQ, BAM, VCF, H5AD, AnnData, Seurat, etc.). Experience with HPC
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to appoint a Postdoctoral Researcher, 100% (TV-L E13) to work on the interdisciplinary research project “NeuroTranslate: Embedded Ethics for Trustworthy, Legitimate, and Fair AI-based Neural Decoding” within
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sustainability issues. In particular, the “Probability/Optimization” group focuses on the theoretical understanding of algorithms used in machine learning, for training large neural networks and tuning
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and physical verifications (DRC and LVS) using Calibre and Quantus. Knowledge of neural networks and neuromorphic systems is a strong advantage. Team player with strong collaboration skills. All
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of improving human health. Aligned with Rutgers University–New Brunswick and collaborating university wide, RBHS includes eight schools, a behavioral health network, and five centers and institutes that focus
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Job Purpose To make a contribution to an ERC-funded project Dynamic network reconstruction of human perceptual and reward learning via multimodal data fusion, working with Prof. Marios Philiastides