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, execution efficiency, and live trading performance. What you can expect Modelling. Apply probability theory, statistical analysis, and machine learning techniques to analyze and interpret market behavior
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the controlled flow at tunable temperature and photopolymerization of the precursor. The practical work will be complemented by fluid mechanics computer simulations, including solutions employing machine learning
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background in CMOS/VLSI design, computer architectures (preferred RISC-V architecture), and deep learning principles. Experience with industry-standard EDA tools such as Cadence suite: Genus, Virtuoso, Spectre
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described in the project overview. Owing to the current composition of the project team, there will be a mild preference for candidates opting for project 2 on “Models and machine learning”. An explanation
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Applicants should hold a relevant MSc degree in electronics, electrical engineering, computer engineering, or related fields. Required Qualification: Solid background in digital CMOS design and deep learning
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, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical
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) List of award winners Our sponsorship The award amount is €45,000. Award winners are also invited to conduct a research project of their choice at a research institution in Germany in cooperation with
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. Specific projects seeking applications are: Accelerating the discovery of inorganic solar-cell materials via a closed-loop, fully robotic synthesis–characterisation platform driven by multi-agent machine
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At the Leibniz Institute of Plant Biochemistry in the Department of Bioorganic Chemistry a position is available for a PhD in Machine Learning for Enzyme Design (m/f/d) (Salary group E13 TV-L, part
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Description At the Leibniz Institute of Plant Biochemistry in the Department of Bioorganic Chemistry a position is available for a PhD in Machine Learning for Enzyme Design (m/f/d) (Salary group E13