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Field
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Retrieval-Augmented Generation (RAG) for data retrieval and knowledge inference implementation of your machine learning pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with
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need expert knowledge in bioinformatic data analysis. Strong expertise in multi-omics data analysis (using R and Python) and a deep understanding of machine-learning models are must-criteria
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, ecosystem function measurement); Basic skills in satellite image analysis; Affinity and experience with data analysis and visualization in R, Python or equivalent; Excellent English language skills, both
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but not mandatory). A strong interest in materials simulation, quantum materials, and condensed matter physics. Experience with Python. Fluency in English is required. Finnish language is not required
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experience of data science analytical packages and R or Python would be advantageous. Funding The project is funded by the UKHSA and Nottingham University Business School. As a UK public body, UKHSA can only
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marine power and control systems. Interest and experience with experimental work and laboratory-based research. Proficiency in data analysis and relevant programming tools (Python, MATLAB, etc.). Relevant
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-on fabrication of TEM supports and MEMS devices. You have good Python programming skills. Experience with cleanroom processes (lithography, etching) or MEMS is a plus. You are motivated to work in a
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(e.g., Python, R, MATLAB) Background or interest in nature-based solutions and optimization techniques Experience in working with climate model outputs and scenario analysis is an advantage A proven
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of the Norwegian grading scale. Solid programming skills, preferably in Python. Hands-on experience in database programming. Experience in installing, training, and using large foundation/language models. Personal
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laboratory-based skills Programming skills (Matlab, Python, or similar) Applicants with a good 2.1 degree are also encouraged to apply if they can demonstrate excellent laboratory skills through previous