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Field
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-on experience in rodent in vivo and ex vivo imaging, microsurgery, eye and brain dissection, and histology. Use advanced microscopy, ImageJ, R, MATLAB and core software such as Word, Excel, Prism and Adobe tools
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contexts, or a strong motivation to explore how different groups of learners appropriate AI-based tools. A strong technical background, ideally in computer science, software engineering, human-computer
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Previous experience in protein biochemistry, LC/MS based mass spectrometry Previous experience in handling omics data analysis using software Experience in R and python is a plus but not mandatory Good
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well as social inequalities, class divisions, social movements, or gender/queer studies; the ability to use data analysis software (e.g. Atlas.ti, MAXQDA), demonstrated by the use of such software in previous
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compartmental models for RSV developed within the STAMP-RSV program by tailoring an established software library for individual simulation to the Australian RSV transmission context. Information to parameterise
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 2 months ago
. Lab Research: • AI-Driven Algorithms & Software: Develop deep leering/machine learning/statistical based algorithms to elucidate lncRNAs, fusion transcripts, RNA modifications, and circular RNAs in
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for diagnostics and therapy Conduct comprehensive bioinformatic analysis utilising established methodologies and tools (e.g. R, Python, MATLAB and other relevant bioinformatics software packages) Collaborate
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, software, and High Performance Computation (HPC) infrastructure; • Excellent scientific infrastructure; • Participation in project meetings and international conferences; • Flexible working hours
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team of engineers, analysts, and software developers. GAC works to develop and apply state-of-the-art energy system modeling techniques, software, and simulations for breakthrough engineering and techno
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of data scientists, software engineers, and experimental researchers on topics including: Developing multi-scale and multi-modal representation learning methods for scientific imaging data (e.g., SEM