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applicant will also bring significant experience with general software engineering, with working experience with Cloud Platforms (Google Cloud Platform, AWS, Azure), alongside a strong understanding of recent
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, or reinforcement learning Proficiency in Python and contemporary software ecosystem for optimization, dada management, and API development Experience with teaching and supervision Experience with IoT, edge computing
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. Desirable criteria Experience working with generative models or large language models Experience with GPU-based model training or cloud computing Knowledge of synthetic biology or regulatory sequence design
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streams—including data streaming to cloud databases, scientific visualization, and integration of machine learning workflows. Development of additional modules within commercial FE software (ABAQUS, MSC
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process, and subsequently implement this infrastructure on top of an existing cloud infrastructure. You will play a key role in the project's development, ensuring technical tasks and teams work together to
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ideally for both bacteria and yeast, anaerobic and aerobic on pilot scale (up to 100L) Interpret data obtained during and after fermentation processes and develop KPIs Document work in cloud-based
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. The research should focus on low-power embedded systems, multimodal sensing (including wearable shoe-based platforms), and edge-cloud computing with serverless and federated learning techniques. You will work
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of the infrastructure. System development in relation to help building research e-infrastructure systems or other systems to enable users to take advantage of HPC systems. Experience with cloud computing platforms (Azure
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performance computing) services. We are looking for a new colleague for a newly established position, someone with insight into and interest in the technical aspects of FAIR data management solutions and