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Field
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Grant, focusing on the development of novel deep learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted
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(HPC) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution
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of this annex, as well as to: Programming in Python and R. Statistical classification and machine learning methods: SVM, neural networks and logistic regression. 3.2. Qualification: Official Master’s degree in
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from visual and auditory cortices recorded over multiple days Apply and adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural
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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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experiments. Data & Analysis: • Collaborate with data scientists to analyze host and microbial data using statistical, bioinformatic, or machine learning approaches. • Contribute to the integration of spatial
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experts and stakeholders, presenting and modeling lesson plans via professional development workshops, and recording lesson plan development processes for grant reporting and public communications
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one of the following analysis techniques (multiple preferred): normative modelling, dimensionality reduction techniques, machine learning, deep-learning, state space modelling, advanced statistics
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cybersecurity research. Who you are: You have BS in machine learning, cybersecurity, statistics, or related discipline with ten (10) years of experience; OR MS in the same fields with eight (8) years
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control under high inverter-based resources (IBRs). • Develop and apply artificial intelligence (AI)/machine learning (ML) techniques for power system planning, operation, control, and cybersecurity