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Field
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theoretical understanding of statistical machine learning methods relevant to the project: Bayesian learning, machine learning, spiking neural networks. Experience of programming (e.g. with Python) and data
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us to explore the relation between the degree and type of processing, and the foods that result from their use. The results will be used for data machine learning, in collaboration with other partners
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worldwide, leveraging industry-standard tools and technologies to ensure the quality and reliability of the developed prototype hardware implementation. Qualifications: PhD in Electronics/Computer Engineering
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didactic skills High written and oral expression skills Computer user skills Excellent command of English Ability to work in a team We also expect: Teaching experience / experience with e-learning Experience
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is connected to the vibrant local ecosystem for data science, machine learning and computational biology in Heidelberg (including ELLIS Life Heidelberg and the AI Health Innovation Cluster ). Your
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data collection approaches. Familiarity with or strong motivation to learn machine learning or advanced data analytics for pattern detection and forecasting in environmental data. Familiarity with
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score derivation and validation, and other relevant analyses. Develops R or Python scripts for data analysis, statistical modeling, and machine learning techniques, ensuring reproducibility and efficiency
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record of peer-reviewed publications. A background is required in computer programming (including Julia and/or C/C++), applied mathematics and statistics. Please upload your application materials via
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interest and documented skills and experience in using computer-based tools to analyse, simulate and predict capture performance of active and passive fishing gears. A track record of publishing in peer
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neuro-adaptability with changes in cortical manifestations during an intervention (e.g., non-invasive brain stimulation) for symptom reduction. Large-scale data analysis (e.g. machine-learning) will