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Field
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the use of machine learning for regression tasks. Demonstrated proficiency with Python or similar scientific programming environment. Ability to work with large datasets, develop reproducible workflows and
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variables. Experience with Matlab, Python... A demonstrable interest in atmospheric physics and offshore wind turbines. Good organizational skills and ability to work independently. Good command of written
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statistical software such as R, Python, SPSS, or Stata. Affinity with issues of climate justice, social inclusion, migration, and/or community-based research. Experience conducting comparative or mixed-methods
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, or (applied) physics. Excellent lab-experiences Advanced mathematical, analytical skills, and programming skills, e.g., in Python. A keen interest in interdisciplinary research. An excellent command of English
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methods for audio data. A publication record in ML applied to bioacoustics is a bonus; Good programming skills in Python. Familiarity with ML frameworks (PyTorch, Tensorfiow, Jax) is a bonus; Demonstrated
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English. Ability to supervise students or early-stage researchers. Affinity with data processing techniques and programming (Matlab and/or Python). Hands-on with laboratory work, skilled in experimental
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/or Python). Hands-on with laboratory work, skilled in experimental design. Creativity, assertive attitude, and perseverance. The selection process will start immediately; the intended start date is in
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to damage or loss data; Experience with programming (Python or R) and working with large datasets; Ability to design and evaluate field experiments or quasi‑experimental studies is an asset; Strong written
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expression systems and/or liposomes. You take an interest in liquid handling robots, Python and Bayesian optimisation. You are a team player and enjoy working in a multidisciplinary environment. You have a
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with user studies, preferably qualitative methods such as interviews, think-alouds or focus groups; significant coding experience in Python; experience with LLMs, in particular as part of agentic systems