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climate states. Key responsibilities To carry out and complete your own research towards a doctoral degree. Design, run, and analyse equilibrium simulations with a coupled Earth-system model of intermediate
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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
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academic records (including transcripts) at masters level or equivalent degree level. • Creative and innovation potential, ability to think out of the box. • Motivation of the candidate for inter
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, HSPICE, or similar IC design tools Knowledge in at least one area as an advantage: deep learning hardware development memory technology CMOS technology Analytical and structured thinking paired with strong
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engineering a strong background in digital design, hardware description languages (e.g. Verilog, VHDL, SystemC), reconfigurable architectures (e.g. FPGA, CGRA) What we expect from you: above-average degree
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Requirements: excellent university degree (master or comparable) in computer engineering or electrical engineering a strong background in digital design, hardware description languages (e.g. Verilog, VHDL
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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 Design analog and mixed-signal circuits, such as data converters
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responsibilities: To carry out and complete your own research towards a doctoral degree. Design, run, and analyse equilibrium simulations with a coupled Earth-system model of intermediate complexity to investigate
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as well. Full-time / part-time full-time Programme duration 6 semesters Beginning Winter semester Application deadline If you intend to secure external funding or apply for a Research Associate
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Responsible planning and implementation of experiments within the framework of the scientific project plan Develop and establish new methods in the laboratory to enable new research approaches Isolation