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(perception, learning, planning) on physical robots. • Use evolutionary algorithms to optimize both the robot’s body and brain together. • Apply quality-diversity methods to discover a wide range of high
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desirable. Experience in one or more of the following is an advantage: finite-sample statistical theory, concentration inequalities, statistical optimality theory, sequential inference, computational
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, and precision oncology. Responsibilities Develop and optimize in vitro models and methodologies to investigate cytotoxic lymphocyte-mediated cancer cell death. Perform genome-wide CRISPR screens
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-healing functionalities embedded for battery longevity with manufacturability and economical recyclability) funded by the European Commission. The candidate will focus on: Electrode development Optimization
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experience with R or Python programming is also very desirable. Experience in one or more of the following is an advantage: finite-sample statistical theory, concentration inequalities, statistical optimality
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program though the Research Council of Norway. The candidate will focus on development of solid-state electrolytes, optimization of materials using ALD, electrochemical evaluation and operando
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on robot development and autonomous navigation but based on the interests of the PhD fellow there are also opportunities to investigate planning and optimization of sensor placement, human-robot interaction