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: Essential experience and skills: You have a PhD in Bioinformatics, Computational Biology, Biostatistics or in a related quantitative field (e.g., Statistics, Mathematics, Physics), and a passion for problem
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plus. Candidates with expertise in either modality are also encouraged to apply. Ideally, applicants for the position should satisfy the following requirements: PhD degree in hearing science, audiology
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perception will be a plus. Candidates with expertise in either modality are also encouraged to apply. Ideally, applicants for the position should satisfy the following requirements: PhD degree in vision
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health, though expertise in these areas is not required for applicants. The position will involve primary responsibility for data organization and statistical analysis, as well as co-authorship
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requirements: PhD degree in vision science, psychology, cognitive (neuro)science, or a related field Expertise in visual perception. Experience with auditory or multisensory perception is a plus. Good command of
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plus. Candidates with expertise in either modality are also encouraged to apply. Ideally, applicants for the position should satisfy the following requirements: PhD degree in hearing science, audiology
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to bridge research and practical applications, while contributing to the digital transformation of the building and energy management sectors. Your primary tasks will be to: Apply advanced statistical and
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statistical and machine learning techniques for dynamic energy system modelling Develop advanced optimization algorithms for building energy management and control (e.g., MPC, RL) Develop and evaluate digital
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Postdoc in Psychiatric Epidemiology: Linking Register and Trial Data to Study Postpartum Depressi...
closely with a PhD student and the broader project team and will contribute to the implementation and follow-up of the randomized controlled trial evaluating internet-based therapy for mothers with
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statistical analyses for the tasks. Based on your competence and interests, your tasks will include: Develop and use epidemiological models (for example regression models or SIR-models), including for “what