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the kinetics of enzymatic polymerization processes in biological environments. Expand and tailor enzyme repertoires to optimize functionality and electrode performance. Collaborate within a multidisciplinary and
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in mouse models and cell cultures. Analyze and interpret omics data using bioinformatic pipelines in Python and R. Perform experiments in cell culture and animal models to validate the findings
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large-scale genomics or molecular cell biology techniques Ability to analyze large-scale data Collaborative abilities Independence Strong publication record Strong oral and written communication skills
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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
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collaboration with the Multiscale Inorganic Materials group, both part of the Division of Energy and Materials at Chalmers . The two groups together comprise nine senior researchers and 27 PhD students and
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(e.g. high strength steels, aerospace materials, refractories, porous ceramics and energy harvesting materials). Read more here Subject description Engineering materials includes the field of material
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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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evaluation process etc please visit: ambercofund.eu. Qualifications Minimum requirements are: Candidate needs to have a maximum 8 years after a doctoral degree (PhD), as required by the project Grant Agreement
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energy use and minimizing environmental impact. This collaborative effort brings together expertise in materials science, physics, polymer chemistry, and composite mechanics, with a particular emphasis on
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clinical applications of novel cell and gene therapies in one integrated setting. In this environment, our research group focuses on combining novel genome engineering tools (e.g., CRISPR-based) and