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of high-throughput systems is maximized using design of experiment (DoE) approaches in order to efficiently explore the high-dimensional parameter space of the electrolysis processes. DoE is required
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field. You have knowledge of the functionality of CMOS digital circuits and technologies for implementing CMOS. You'll also have initial experience in the digital field with regard to circuit design of
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(required) German language skills are desirable Ability to work both independently and as part of a collaborative team High motivation for interdisciplinary research and experimental innovation Experience
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, System Verilog and programming by using Perl or Python Preferably circuit and layout design experience in Cadence Virtuoso, Tanner EDA or any other full-custom design tool Strong problem-solving and
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the perspective of energy infrastructure. Using the existing energy system models of ICE-2, you will develop scenarios to analyze the effects on the German energy system and its robust design in a European context
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, information technology or comparable with a master's or diploma degree Experience in FPGA design for different technologies and suppliers like Intel PSG (Altera), Xilinx or Lattice Knowledge in synthesis and
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of ASIC/ FPGA SoC architecture and digital design Proficiency in hardware description languages such as System Verilog, Verilog, or VHDL Programming knowledge in Python and C Experience on frontend and
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tomography. To this end, you will utilize pattern matching to identify characteristic sample features both in X-ray tomographic images and the focussed-ion beam workstation in which the samples for electron
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At the Leibniz Institute of Plant Biochemistry in the Department of Bioorganic Chemistry a position is available for a PhD in Machine Learning for Enzyme Design (m/f/d) (Salary group E13 TV-L, part
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Your Job: Digital methods for inverse materials design are essential to efficiently create new, sustainable and recycling-adapted structural metals. Alloys with a reduced number of elements, so