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from neuromodulation studies; specific experience analyzing fMRI BOLD signal data; specific experience analyzing MRI structural and tractography data, particularly with a focus on deep brain About the
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department whose faculty’s research ranges from paleoclimate to deep earth dynamics. The Department is in the School of Earth & Environmental Sciences (www.cse.umn.edu/esci), which also includes the Institute
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
computer vision and/or audio(-visual) machine learning (e.g., multi-object tracking, speaker/source localisation, multimodal fusion). Experience with deep learning frameworks (PyTorch or equivalent) and
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docking to investigate viral spike/capsid architecture, thermal stability, and immune epitope accessibility. AI/Deep Learning: ML/DL model development (e.g., PyTorch, Lightning) for predicting antigenicity
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decisions. Adheres to University and unit-level policies and procedures and safeguards University assets. Develop various machine learning and data mining models including deep convolutional neural networks
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/Qualifications Strong research track record in AI and scientific applications. Excellent knowledge of Machine Learning and Deep Learning. Strong Python programming skills. Experience with PyTorch, TensorFlow
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strategies. Duties and Responsibilities Design, implement, and evaluate deep learning models for spatiotemporal data, with an emphasis on medium-scale foundation models. Leverage model embeddings in causal
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at APC Microbiome Ireland. Together they will be focused on interdisciplinary approaches to unravel how the microbiome communicates with the brain. The successful candidate will bring deep expertise in
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IOCB Prague (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences) | Czech | 2 months ago
-scale deep learning, ideally applied to protein or nucleic acid sequences Ability to work independently and to translate diverse scientific use cases (brought by wet-lab and computational colleagues alike
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/Deep Learning: ML/DL model development (e.g., PyTorch, Lightning) for predicting antigenicity, genetic variation, and viral evolution. Genomics & Bioinformatics: Viral genome-based evolutionary analysis