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molecular level. To yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at leveraging graph-theoretic approaches to analyze and
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societal aspects (ELSA) of NeuroAI in the life sciences and biomedicine. The project focuses on (i) neu-romorphic computing inspired by the human brain and (ii) AI-enabled neurotechnologies for clinical and
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theoretical and computational methods to describe electronic, optical, and transport properties of complex materials. The first advertised project (a) is dedicated to the methodological enhancement
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use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission analysis, and infrared thermography. Industry
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team. You will be enrolled in the TUM Graduate School with its structured doctoral program and professional-skills courses. You will contribute to an French-German collaboration on next-generation
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. How to Apply: Please send your application including all relevant documents (CV, transcripts, references) to Lehrstuhl für Nachhaltige Mobile Antriebssysteme, z.Hd. Adrian Dörfler, Schragenhofstr. 31
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master’s degree in mathematics, theoretical computer science, machine learning, or a closely related field. Strong background in discrete optimization, algorithms, or reinforcement learning. Good programming
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. The application should contain a letter of motivation, a CV including publication list, (degree) certificates, and at least two possible references. Please send your application as a single pdf-document by email to
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, Mechanical Engineering, Electrical Engineering, Computer Engineering, or a closely related discipline. Strong research interest in telerobotics, shared control, human–robot interaction, or networked control
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hours and on-campus childcare are available. Applicants with disabilities will be given preference if equally qualified. Application Please send your CV, a motivation letter (max. 1 page), and the names