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(meta-)genomes Experience in the computer-assisted analysis of large biological datasets (e.g., using R, Python, and Bash/Linux environments) Very good written and spoken English skills Ability to work
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Kurt Schwabe Institute for Measuring and Sensor Technology e.V. (KSI) Meinsberg | Waldheim, Sachsen | Germany | 3 months ago
- Contribution to a machine-learning-driven experimentation platform - Comparison of automated and manual experiments Your profile: - Master’s degree in chemical engineering, biotechnology, chemistry, physics
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 2 months ago
and advance statistical, image analysis, and machine learning approaches Develop interpretable models that reveal biologically testable relationships and generate new scientific hypotheses Independently
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | 3 months ago
fields Experience with image analysis, or computer vision Good knowledge of basic machine learning techniques, such as variational autoencoder Good presentation and writing skills Proactive, independent
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) is one of the world-leading groups in the field of computer simulations of active, soft and living matter systems, such as cells, bacteria, tissues, and active synthetic colloids. Our aim is to provide
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CFD simulations, led to a shift from performance towards efficiency, defined more precisely as minimisation of computer resources required for a given simulation. Concomitantly, the objective
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diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation, evidence-based policymaking, science-policy communication, and policy brief writing
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FieldComputer scienceEducation LevelMaster Degree or equivalent Skills/Qualifications Strong foundations in Machine Learning and Deep Learning Excellent Python programming skills Experience with PyTorch
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, and related fields (e.g., Graph Machine Learning) Tasks: scientific research in at least one of the above-mentioned research areas collaboration in national and international research projects, possibly
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of machine learning and clinical oncology, with access to a large multimodal research dataset, substantial GPU resources, and a collaborative scientific environment. Your tasks Design and implement LLM-based