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of psychology, digital health, and artificial intelligence to advance the development and validation of voice-based tools for mental health monitoring in diabetes. The project will leverage data from the Colive
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patient clusters and digital phenotypes, leveraging machine learning approaches to identify individuals at high CV risk based on clinical and biochemical markers, immune markers, digital health data (e.g
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for neurodegenerative diseases. Outcomes could be diagnoses of mild cognitive impairment, dementia, and/or Parkinson's Disease, or markers of brain structure and functioning, depending on the dataset
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and neural computation. By leveraging the rich data repository on the larva’s neural connectome and muscular structure, the project seeks to create accurate simulations that can inform biological
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their magnetic state at the nanoscale using scanning NV center magnetometry, both at 4K and room temperature. We will in particular examine domain walls, as their internal structure should give us insight about
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an integrative approach that combines advanced multiomics, spatial transcriptomics, and structural biology with physiological assessments. By selectively perturbing key gene expression programs during homeostasis
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channels Predict variant-specific protein structures using AlphaFold Co-develop machine-learning approaches to incorporate quantum effects in molecular dynamics simulation Predict variant-specific drug
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Cognitve Sciences and more specifically the Institute of Health and Behaviour. The person will work under the supervision of Dr. Andreia Costa, Principal Investigator of the project "Bridging Gaps
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on questions of lifespan development, views on aging, ageism, preparation for old age, health and well-being, as well as personality and identity development in later life. The candidate will be encouraged
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(e.g., by enhancing learning, improving mental health, teaching green skills, strengthening cybersecurity). The xCIT team is part of the Department of Behavioral and Cognitive Science of the Faculty