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/aptamer-sensors Your Profile: Bachelor`s degree (or equivalent) in the field of biomedical engineering, biotechnology, chemistry, or similar Hands on lab experience developing biosensors/sensors is required
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laboratories and testbeds for optical communications in Europe and maintains a complete library of optical communications digital signal processing algorithms. We are recruiting students in the field of digital
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strategies, and cross-functional collaboration — shaping the future of quantum computing. We holistically address business-relevant challenges using innovative quantum computing algorithms and demonstrate
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Group (EASE IRTG), Empowering Digital Media (EDM), the Research Training Group HEARAZ , the Research Training Group KD²School (KD²School), π³: Parameter Identification – Analysis, Algorithms
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- conducting processors with respect to practical short-depth (NISQ) quantum algorithms Cooperate and actively work with experimental partners developing quantum processors using these technological platforms
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dynamics or Hamiltonian eigenstates with classical and quantum algorithms Comparison of quantum algorithms with classical baselines Analysis and documentation of results What you bring to the table Student
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Max Planck Institute of Biophysics, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | about 1 month ago
(imaging) data analysis is preferred. Prior experience in microscopy is desired but not required.) Development of 3D diffusion-based single-molecule sensors (the candidate with theoretical knowledge and
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you will do Driving innovative AI research through the development and implementation, practical application, theoretical analysis and evaluation of AI algorithms Use of XAI tools to explain machine
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control, state estimation, and path planning algorithms for single and multi-agent robotic systems (UAVs). develop and train AI models for practical applications such as real-time object detection and
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms