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Field
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data management and machine learning is also preferred. An interest in energy system topics such as the green transition, sustainable energy systems, digital energetics etc. is preferred. Experience
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. The postdoctoral fellow will join a research group with potential access to large, administrative data sets therefore prior experience with big data and administrative data will be advantageous. via Unsplash
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for future career development in both academia and tech. Profile Required: PhD in ML, computational neuroscience, physics, engineering, or related field Strong experience in machine learning (PyTorch
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/Resume. Required Education and Experience PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related field. Preferred Qualifications Strong publication record in top-tier AI
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at international conferences. You hold a PhD in computational biology/chemistry, machine learning or a related quantitative field. You have a solid publication record and demonstrated experience with advanced
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systems, large multimodal foundation model training and/or finetuning, and continuous learning pipelines. Experience in multi-modality data analysis (e.g., image, video, text). Experience working in
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using liquid biopsy next generation sequencing data for cancer diagnostics. About You Must have a strong background in next generation sequencing data analysis/machine learning, cancer and/or genome
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energy consumption in information processing and machine learning (e.g., arXiv:2308.15905); Quantum phenomena in information processing: exploring how quantum effects can be utilized to process information
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generation, media forensics, anomaly detection, multimodal learning with an emphasis on vision-language models, computer vision applications for space. Key responsabilities: Shape research directions and
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with expertise in the following four areas: (1) working with large-scale digital trace data; (2) building and running natural language processing and machine learning workflows; (3) experimental design