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for iii) gastric pacemaking and iv) brain-computer interfaces . These four teams are supported by the platforms for 1) opsin engineering, 2) gene transfer, 3) disease models, 4) immune-phenotyping and 5
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additional independent research groups (approx. 200 employees, including around 40 doctoral students). Research at the IPB aims to understand the (bio)chemical basis of plant resilience and performance in
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of four scientific Departments and additional independent research groups (approx. 200 employees, including around 40 doctoral students). Research at the IPB aims to understand the (bio)chemical basis
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, chemical engineering, biochemistry, biotechnology or a similar field of study Relevant experience in the analysis of organic compounds Knowledge of electrochemistry is desirable Independent and conscientious
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Engineering or Chemistry, Physics or Informatics Experience with experimental work and characterization techniques Demonstrated experience in programming, particularly in Python, with a robust understanding of
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Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg | Magdeburg, Sachsen Anhalt | Germany | 4 days ago
hold a master's degree in the engineering or natural sciences, preferentially in chemical or process engineering, physics, chemistry or related disciplines with grades above average, have a strong
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preparing publications in peer-reviewed journal. Your Profile M.Sc. in (Electro)Chemistry, Physics, Electrical Engineering, Materials Sciences or equivalent qualification Experience in setting-up, operating
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. Work with large language models (LLM) and deep learning algorithms to drive the inverse design of materials and uncover new physical and chemical phenomena. Ensure the integration and effectiveness of AI
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technology and recycling group. It focuses on applications where its expertise in materials science, chemistry and metallurgy can make a real difference. Its activities are organised into three business groups
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the standardization and harmonization of data across platforms. Work with large language models (LLM) and deep learning algorithms to drive the inverse design of materials and uncover new physical and chemical