1,090 algorithm-development-"Prof"-"Washington-University-in-St"-"Prof"-"Prof" positions in Germany
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for autonomous systems. Our focus lies on safety-critical applications in the fields of automation, mobility and health. We develop reliable software technologies with a benefit for humans. For example, we conduct
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will work in a group with other students and take on specific tasks. The aim is to analyse the robot's capabilities and to implement algorithms that enable the robot to be used sensibly in applications
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Fraunhofer SIT is one of the leading research and development institutes for secure information technology in Germany and Europe. The focus is on applied research and the development of innovative
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PhD student (m/f/d) in the field of organoids, stem cell research and lung development and disease The positions are part of the Professorship for Lung Organoids and Tissue Engineering for Advanced
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Overview Eine Druckversion dieser Ausschreibung (PDF) steht Ihnen in den weiteren Registerkarten (z.B. „Bewerbungsvoraussetzungen“) zur Verfügung. A print version of this announcement (PDF) is available in the further tabs (e.g. “Application requirements”). Objective Advanced students of...
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algorithms for very high speed coherent passive optical networks (VHSP) Implementation of DSP algorithms in MATLAB/C++, that can be used for ASIC development Development of our DSP software tools Research work
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algorithms and Quantum Imaginary Time Evolution. What you bring to the table Experience with Python programming Experience in programming quantum algorithms with Qiskit, Pennylane, or similar Prior knowledge
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Analysis of Microscopic BIOMedical Images (AMBIOM) You will be responsible for Developing new machine learning algorithms for microscopy image
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on these considerations, solutions will be developed and implemented to create an advanced testing platform for the comprehensive evaluation of the long-term stability of invasive brain-computer interfaces. Your task will
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project within the SusMax network focused on developing interpretable machine-learning frameworks for kinetic multiphase reaction-network discovery in the catalytic conversion of renewable feedstocks