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Field
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of embedded machine learning, neuromorphic hardware and deep learning accelerators. Want to get more information? Click here. What you will do Responsible for RTL design (VHDL, Verilog) of digital blocks and
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strengthen the data science and machine learning activities of the IAS-9 with exciting new topics. You will work in a multidisciplinary team of enthusiastic data scientists, software developers and domain
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. Strong coding skills for programming neural networks, machine learning and machine learning software frameworks (e.g. PyTorch or Jax) is a must. The ability for creative and analytical thinking across
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. Strong coding skills for programming neural networks, machine learning and machine learning software frameworks (e.g. PyTorch or Jax) is a must. The ability for creative and analytical thinking across
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Requirements: Applicants should hold an MSc or Diploma in Engineering, Computer Science or a related discipline. Background in Machine Learning and Artificial Intelligence. Strong programming skills (Python
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of embedded machine learning, neuromorphic hardware and deep learning accelerators. Want to get more information? Click here. What you will do Responsible for RTL design (VHDL, Verilog) of digital blocks and
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assessment of chemical plants using HAZOP analysis Use of process modeling and simulation to enhance quantitative assessments Use of machine learning to support HAZOP discussions with the aim of obtaining a
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08.09.2021, Wissenschaftliches Personal The Professorship of Machine Learning at the Department of Electrical and Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13
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on machine learning assisted PSPR optimization of recently developed lean Mg-0.1Ca alloy produced by PBF-LB. After identification of the most relevant parameters adopting a design of experiments
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
properties (hardness, yield and tensile strength) and corrosion profile (rate and localization). This work focuses on machine learning-assisted PSPR optimization of recently developed lean Mg-0.1 Ca alloy