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Field
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the wild? You are excited about deploying autonomy algorithms on real platforms and validating them in demanding real-world environments? We are looking for an outstanding and highly motivated doctoral
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machine learning, deep learning, or AI. Solid mathematical, algorithmic, or physics background, distinct analytical skills. Very good programming (Python, C++) and computer (Linux, Windows) skills
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of research and innovation! Be part of change Design and development of IP cores for low-latency, high-throughput digital signal processing Simulation and verification of the implemented algorithms with test
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processing Simulation and verification of the implemented algorithms with test vectors Integration of IP cores into an OFDM-based Physical Layer Debugging of implemented designs in a LiFi testbed Collaboration
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processing, algorithm design, optimisation and simulation, software engineering and automation and control systems. An overview of the current PhD research projects is given here: https://www.dashh.org
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Your Job: Join our team as a dedicated scientist and contribute to our exciting research projects. Our work focuses on models and algorithms for supervised and unsupervised learning. We devise deep
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algorithms, computational complexity theory, and information theory Relevant coursework and experience in spiking neural networks, and statistics A strong electronics background, including experience in
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allow brute-force exploration it is important to select the most informative experiments. Bayesian optimization, an iterative global optimization algorithm, has been shown to be a more efficient in
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 2 months ago
systems. Key Responsibilities Develop graph-based (multi-)omics analysis algorithms Benchmark graph-theoretic against graph-ML approaches Analysis of food-related (multi-)omics data Your Profile The ideal
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predict food-effector systems. Key Responsibilities • Develop graph-based (multi-)omics analysis algorithms • Benchmark graph-theoretic against graph-ML approaches • Analysis of food-related (multi-)omics