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– protein interactions or enzyme optimization. Main responsibilities The successful candidate will use and develop methods within one, or preferably multiple, of the following categories: Sequence library
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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the use of crystallographic software and data processing pipelines Experience working with computation clusters and managing large datasets Proven ability to develop, maintain, and optimize scientific
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, including finite-element simulation and topological optimization of light guidance in HCFs, and numerical simulation of thermo- and fluid dynamics under fiber-drawing processes. Apart from the main tasks
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is expected that they will actively and creatively develop and optimize the detailed methods to pursue the overall project goals and, after a training period, independently analyze genomic data using
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candidate will work fulltime on the above-outlined research project. It is expected that they will actively and creatively develop and optimize the detailed methods to pursue the overall project goals and
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contribute to the activities including TES unit development, laboratory testing and techno-economic analysis to identify optimal integration opportunities. Cooperation with industrial and academic national and
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control for medical robotics in the context of cardiovascular technologies. The goal is to innovate control systems for optimized interaction of soft cardiovascular pumps and wearable biofeedback systems
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responsibilities of the successful candidate will be: Perform research in designing, synthesizing, and characterizing electrocatalysts for optimized amine-mediated CO₂ reduction. Perform mechanistic studies and
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topological optimization of light guidance in HCFs, and numerical simulation of thermo- and fluid dynamics under fiber-drawing processes. Apart from the main tasks above, the postdoc can be involved in