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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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in heterogeneous integration. Other related functions as assigned. Required Qualifications A PhD received within the last 3 years in Electrical and/or Computer Engineering or a closely related
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will work on multiple projects funded by NIH/NHGRI. The objective of the position is to develop novel statistical methods and computer software and analyze large scale biological data from biobanks
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challenge. This project aims to explore data-driven Artificial Intelligence/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines
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approaches (based on functional programming abstractions) to optimize the implementation of machine learning models and other digital signal processing algorithms on a specific FPGA architecture to fit within
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is connected to the vibrant local ecosystem for data science, machine learning and computational biology in Heidelberg (including ELLIS Life Heidelberg and the AI Health Innovation Cluster ). Your
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projects aimed at developing resource responsible, economically viable solutions to challenges in the food production and critical material systems. There will be the opportunity to mentor students, teach
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. You will perform microstructural characterization of dry coated electrodes using physical and machine learning based methods and the electrochemical assessment of the electrodes in battery cells. Your
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interest and documented skills and experience in using computer-based tools to analyse, simulate and predict capture performance of active and passive fishing gears. A track record of publishing in peer
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cutting-edge big/deep data analysis methods, including machine learning and artificial intelligence. The ideal candidate will therefore have a strong background in data science and in the application and